The University of Dodoma University of Dodoma Institutional Repository http://repository.udom.ac.tz
Natural Sciences Master Dissertations
2018 Vegetation diversity and carbon stock of natural regeneration woodlands of Kishapu district, Tanzania
Kwilasa, Gisandu M.
The University of Dodoma
Kwilasa, G. M. (2018). Vegetation diversity and carbon stock of natural regeneration woodlands of Kishapu district, Tanzania. Dodoma: The University of Dodoma http://hdl.handle.net/20.500.12661/1762 Downloaded from UDOM Institutional Repository at The University of Dodoma, an open access institutional repository. VEGETATION DIVERSITY AND CARBON STOCK OF NATURAL
REGENERATION WOODLANDS OF KISHAPU DISTRICT,
TANZANIA
BY
GISANDU MALUNGUJA KWILASA
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT FOR
THE DEGREE OF MASTER OF SCIENCE IN BIODIVERSITY
CONSERVATION
THE UNIVERSITY OF DODOMA
OCTOBER, 2018 DECLARATION
AND
COPYRIGHT
I, Gisandu Malunguja Kwilasa, declare that this dissertation is my original work and that it has not been presented and will not be presented to any other University for a similar or any other degree award.
Signature: ……………………………………………
No part of this thesis/dissertation may be reproduced, stored in any retrieval system, or transmitted in any form or by any means without prior written permission of the author or the University of Dodoma. If transformed for publication in any other format shall be acknowledged that, this work has been submitted for degree award at the University of Dodoma”.
i CERTIFICATION
The undersigned certify that they have read and hereby recommend for acceptance by the University of Dodoma dissertation entitled “Vegetation Diversity and Carbon
Stock of Natural regeneration Woodlands of Kishapu district, Tanzania” in partial fulfilment of the requirements for the degree of Master of Science in Biodiversity
Conservation of the University of Dodoma.
DR. CHRISPINUS D.K. RUBANZA
Signature………………………………………………Date: ……………….
(SUPERVISOR)
DR. MHUJI B. KILONZO
Signature…………………………………………………Date: ………………...
(SUPERVISOR)
ii ACKNOWDLEDGEMENT
First and foremost, glory is to God for his divinity power which has enabled me to face all difficulties during the preparation of this involving work. Secondly, I am very grateful to my supervisors Dr. Chrispinus D.K. Rubanza, and Dr. Kilonzo, M. both of the Department of Conservation Biology, School of Biological Science, The
University of Dodoma for their significance, valuable scientific guidance. Their advice, constructive criticisms, commitment and patience were my modifying tool to enhance the success of this work.
Indefinite thanks are handled to Prof. Jonathan L. Kabigumila of the University of
Dodoma for his indispensable time in making valuable professional guidance and corrections. I also express my sincere gratitude to the Management of the
University of Dodoma for offering such a requirement in fulfilling the program from which I have being exposed to the practical knowledge and techniques skills. Special thanks should be addressed to Staff Members at Tanzania Forestry Research
Institute (TAFORI) and the facilitation provided by Natural Resources Management and Agroforestry Centre (NAFRAC) of Shinyanga Region during my study. Mostly
Ms Rose Njawala (Lab Tech), Emmanuel Mbwiga (Forester) and Mr. Mziray (The
Manager) for their heartfelt assistance and friendly cooperation for the success of this study. Last but not least, I am also grateful to Kishapu district council for their cooperation and allowing me to conduct my study in their area. I thank field assistants Mr. Sandu Peter Malunguja and Mr. Mhija Budeba and MSc Biodiversity students 2016/18 for their strenuous encouragement.
iii DEDICATION
This dissertation is dedicated to my lovely Mother Mrs. Sado R. Malunguja, who laid down the foundation of my education with a lot of difficulties. Special dedication to my family members for their support and encouragement during all period of my study which make this dissertation to be completed in time. I also dedicate to my lovely daughter, Glorygigwa for missing me and her tolerant of living alone for most of two years of my study.
iv ABSTRACT
Natural regeneration woodlands have the potential for enhanced carbon sequestration and climate change mitigation although little is known on vegetation species diversity and the associated carbon dynamics. A study was carried out among three selected natural regeneration woodlands (Nyasamba, Bubinza and Ndoleleleji) of Kishapu district aimed (1) to determine plant species diversity, (2) to quantify herbaceous biomass productivity, (3) to determine tree stocking capacity, and (4) to quantify soil organic carbon (SOC). A nested model design comprised of four radii (r = 2, 5, 10 and 15 m) concentric plots were established at 300 m between plots. Inter-transect distance was maintained at 550 m. A sampling intensity of 0.3% which is equivalent to 74 sample plots was adopted. Data on vegetation species diversity, biomass productivity, forest stocking parameters and SOC were computed into means using analysis of variance (ANOVA). Dominant grass species were Aristida spp., Cyperus spp., Cynodon spp. and Cenchrus spp. Forb species were Monechma spp., Leucas spp., Commelina spp., Sida spp. and Cucumis spp. The woodlands were dominated by Acacia spp. (A. drepanolobium, A. nilotica, A. polyacantha, A. senegal, A. seyal, and A. tortilis), Dichrostachys cinerea and Balanite aegyptica. Herbaceous biomass productivity was low (1.15-1.23 t DM/ha), (P˂0.05). The woodlands were characterised by low plant species diversity ranging from 0.11 to 0.48 for Simpson‟s index of diversity (C). Tree stocking parameters depict high tree stocking density of 1021±198 to 2003±295 stems/ha, moderate basal area of 6.11±2.1 to 7.64±3.1 m2/ha, and high tree standing volume ranging from 41.02±13.1 to 50.08±19.5 m3/ha.
Above-ground carbon stock (AGCS), was variable across sites (11.9±3.8-14.5±5.7 t/ha), (P˂0.05). Below-ground carbon stock (BGCS), ranged from 2.97±1.00 to 3.63±1.4 t/ha. Concentration of soil organic carbon (SOC) was variable across soil horizons, with high concentration on the 0 to 30 cm depth (0.12±0.05-0.7±0.07 kg/m2). It could be concluded that natural regeneration woodlands of Kishapu district has a promising herbaceous biomass productivity as well as good tree carbon stocking potential which influences species diversity.
v TABLE OF CONTENTS
DECLARATION ...... i COPYRIGHT ...... i CERTIFICATION ...... ii ACKNOWDLEDGEMENT ...... iii DEDICATION ...... iv ABSTRACT ...... v TABLE OF CONTENTS ...... vi LIST OF TABLES ...... x LIST OF PLATES ...... xiii LIST OF ABBREVIATIONS AND ACRONYM ...... xiv
CHAPTER ONE ...... 1 INTRODUCTION ...... 1 1.1 Background of the study ...... 1 1.2 Statement of the problem ...... 5 1.3 Research objectives ...... 5 1.3.1 General objective ...... 5 1.3.2 Specific objectives ...... 6 1.4 Research hypotheses ...... 6 1.5 Significance of the study ...... 7
CHAPTER TWO ...... 8 LITERATURE REVIEW...... 8 2. 1 Theoretical Review ...... 8 2.1.1 Concepts and definitions ...... 8 2.1.2 Theories underlying the study ...... 11 2.2 Empirical review ...... 12 2.2.1 Ngitili natural regeneration vegetation conservation system ...... 12 2.2.2 Land use and management of Kishapu district ...... 15 2.2.3 Herbaceous, shrubs and tree species diversity of Ngitili natural regeneration woodlands ...... 15 2.2.3.2 Herbaceous species composition ...... 18 vi 2.2.4 Herbaceous biomass productivity of Ngitili natural regeneration woodlands .. 19 2.2.5 Tree stocking parameters of Ngitili natural regeneration woodlands ...... 22 2.2.6 Tree standing biomass ...... 23 2.2.7 Concentration of soil organic carbon of Ngitili natural regeneration woodlands25 2.2.8 Influence of policy and legislation on Ngitili natural vegetation conservation system ...... 27 2.2.9 Conceptual frame work ...... 28
CHAPTER THREE ...... 31 RESEARCH METHODOLOGY ...... 31 3.1 Study area ...... 31 3.1.1 Location...... 31 3.1.2 Climate ...... 33 3.1.3 Edaphic characteristics ...... 33 3.1.4 Vegetation ...... 33 3.1.5 Demography ...... 34 3.1.6 Socio-economic activities ...... 34 3.2 Research design...... 34 3.2.1 Sampling design ...... 35 3.2.2 Study population ...... 36 3.2.3 Sampling frame ...... 36 3.2.4 Sampling unit ...... 36 3.2.5 Sampling intensity ...... 36 3.2.6 Sample size...... 36 3.2.7 Measurable parameters...... 37 3.3 Data collection techniques ...... 37 3.3.1 Herbaceous species composition...... 37 3.3.2 Shrub and tree species diversity ...... 38 3.3.3 Herbaceous biomass productivity ...... 40 3.3.4 Tree stocking parameters ...... 41 3.4 Statistical analyses ...... 47 3.4.1 Herbaceous species composition...... 47 3.4.2 Shrubs and tree species diversity ...... 48
vii 3.4.3 Trees stocking parameters ...... 48 3.4.4 Soil organic carbon ...... 49 3.3.6 Validity and reliability ...... 50
CHAPTER FOUR ...... 51 RESULTS ...... 51 4.1 Herbaceous, shrubs and tree diversity ...... 51 4.1.1 Dominant vegetation species of Ngitili natural regeneration woodlands of Kishapu district ...... 51 4.1.2 Herbaceous species composition of Ngitili natural regeneration woodlands of Kishapu district ...... 51 4.1.3 Tree and shrub species diversity of Ngitili natural regeneration woodlands of Kishapu district ...... 56 4.2 Herbaceous biomass productivity of Ngitili natural regeneration woodlands of Kishapu district ...... 57 4.3.1 Tree stocking density ...... 58 4.3.2 Basal area ...... 59 4.3.3 Tree standing volume ...... 59 4.3.4 Tree standing biomass ...... 60 4.3.5 Tree carbon stock ...... 60 4.4 Concentration of soil organic carbon of Ngitili natural regeneration woodlands of Kishapu district ...... 61
CHAPTER FIVE ...... 63 DISCUSSION OF THE RESULTS ...... 63 5.1 Herbaceous, shrubs and tree species diversity of Ngitili natural regeneration woodlands of Kishapu district ...... 63 5.2 Herbaceous biomass productivity of Ngitili natural regeneration woodlands of Kishapu district ...... 65 5.3 Tree stocking parameters of Ngitili natural regeneration woodlands of Kishapu district ...... 66 5.4 Tree carbon stock of Ngitili natural regeneration woodlands of Kishapu district 66
viii 5.5 Concentration of soil organic carbon of Ngitili natural regeneration woodlands of Kishapu district ...... 67
CHAPTER SIX ...... 68 CONCLUSION, RECOMMENDATIONS AND AREAS FOR FURTHER STUDY68 6.1 Conclusions ...... 68 6.2 Recommendations ...... 69 6.3 Areas for further study ...... 70 REFERENCES ...... 71
ix LIST OF TABLES
Table 1: Number, ownership, and size of Ngitili in Kishapu district ...... 13
Table 2: Land use and management of Kishapu district ...... 16
Table 3: Dominant herbaceous species of selected natural regeneration woodlands
Shinyanga region ...... 17
Table 4: Herbaceous species composition of selected natural regeneration
woodlands of Shinyanga region ...... 19
Table 5: Tree and shrub species diversity of selected Ngitili natural regeneration
woodlands of Shinyanga region ...... 21
Table 6: Tree stocking parameters (standing density, basal area and standing
volume) of selected natural woodlands of Shinyanga region ...... 24
Table 7: Tree standing biomass of selected natural regeneration woodlands of
Shinyanga region ...... 25
Table 8: Concentration of soil organic carbon (SOC) of Ngitili natural regeneration
woodlands of Shinyanga region ...... 26
Table 9: Dominant grass species of selected natural regeneration woodlands of
Kishapu district ...... 52
Table 10: Dominant forb species of selected natural regeneration woodlands of
Kishapu district ...... 53
Table 11: Dominant tree and shrub species of selected natural regeneration
woodlands of Kishapu district ...... 54
Table 12: Herbaceous species composition (%) of selected natural woodlands of
Kishapudistrict…………………………………………………………55
Table 13: Tree and shrub species diversity of selected natural regeneration
woodlands of Kishapu district ...... 57
x Table 14: Herbaceous biomass productivity of selected natural regeneration
woodlands of Kishapu district ...... 58
Table 15: Tree stocking parameters (tree stocking density, basal area, tree standing
volume, and tree standing biomass) of selected natural regeneration
woodland in Kishapu ...... 58
Table 16: Tree carbon stocks of selected natural regeneration woodland of Kishapu
district ...... 61
Table 17: Concentration of Soil organic carbon (kg/m2) of selected natural
regeneration woodlands across soil depth of Kishapu district ...... 62
xi LIST OF FIGURES
Figure 1: The causal-effect relationship ...... 30 Figure 2: Map of Kishapu district to show study sites ...... 32 Figure 3a: Plots distribution of selected natural regeneration woodlands of Ndoleleji/ Shagihilu of Kishapu district ...... 38 Figure 3b: Plots distribution of selected natural regeneration woodlands of Shagihilu in Kishapu district ...... 39 Figure 4: Circular plot showing sub-plots for measurement of tree stocking parameters ...... 42
xii LIST OF PLATES
Plate 1: Field herbaceous species visual estimation on thrown quadrat ...... 40
Plate 2: Measurement of tree Dbh using tree diameter caliper at Bubinza
woodland ...... 44
Plate 3: Pits preparation for soil sampling for bulky density at Bubinza woodland .. 50
Plate 4: Soil sampling using auger at Ndoleleji/Shagihilu woodland ...... 50
Plate 5: Preparation of soil samples for oven dry and laboratory titration to quantify
concentration of SOC ...... 49
xiii LIST OF ABBREVIATIONS AND ACRONYM
AGB Above-ground biomass
BD Bulky Density
BGB Below-ground biomass
C Carbon
C Simpson‟s Index of Diversity
CBFM Community Based Forest Management
CDM Clean Development Mechanism
CERs Certified Emission Reductions
CO2 Carbon dioxide
CRBD Completely Randomized Block Design
CRS Congressional Research Service
DBH Diameter at breast height
DM Dry Matter
FAO Food and Agricultural Organization
GIS Geographical Information System
GPS Global Positioning System
H‟ Shannon-Wiener Index of Diversity
Ha Hectare
IPCC Intergovernmental Panel on Climate Change
JFM Joint Forest Management
NAFORMA National Forestry Resources Monitoring and Assessment of Tanzania
NPP Net primary productivity
OM Organic matter
PFM Participatory Forest Management
xiv REDD Reduction of Emission from Deforestation and Forest Degradation
REDD+ Reducing Emissions from Deforestation and Forest Degradation, as
well as conservation, sustainable management of forests and
enhancement of forest carbon stocks
SOC Soil Organic Carbon
TaTEDO Tanzania Traditional Energy Development Organisation
UN-CBD United Nations Conversion on Biological Diversity
UNFCC United Nations Framework Convention on Climate Change
URT United Republic of Tanzania
WWF World Wildlife Fund
xv CHAPTER ONE
INTRODUCTION
1.1 Background of the study
Forests are important carbon (C) sinks than any other terrestrial ecosystem due to their high C sequestration potential to offset about 18 to 25% t C /ha (Nair et al.,
2009). In Tanzania, like elsewhere in the tropics, forests and natural regeneration woodlands represents an important carbon reservoir component in mitigating emissions of carbon dioxide (CO2) to overcome global warming and climate change
(Burgess et al., 2010). According to the Natural Forest Resource Monitoring and
Assessment (NAFORMA) report (NAFORMA, 2015) total area of forests in
Tanzania is estimated to 48.1 million ha, which is 55% of the total land area (88.02 million ha). Natural regeneration woodlands occupy about 74 % of the forested area
(NAFORMA, 2015), the remaining is on bushlands, water catchment, croplands and grassland. According to United Republic of Tanzania National Report on the
Implementation of the Convention on Biological Diversity report, (URT, 2001), forests and woodlands in Tanzania and East Africa in general are comprised of diverse plant species, estimated to over 10,000 with hundreds being endemic.
However, sustainability of the rich forestry resources of Tanzania including natural regeneration forests of Kishapu district is threatened by the on-going high extent of deforestation, initially over estimated between 350,000 to 500,00 ha/year, (URT,
1998), moderate to high values (300,000 to 400,000 ha/year), (URT, 2001), and a much more accurate 372,871 ha/year documented in 2015 NAFORMA forest inventory report (NAFORMA, 2015).
1 Areas of semi-arid north-western and central Tanzania are dominated by Acacia-
Commiphora woodlands (URT, 2003). In situ vegetation conservation system has been advocated as a quick recovery of degraded forests and has been adopted ethnic communities of Tanzania and even in East Africa (Otsyina et al., 1997). In north- western Tanzania for instance, the Sukuma agropastoral communities practice an in situ vegetation conservation system traditionally known as „Ngitili’.
Ngitili refers to an in situ vegetation conservation system which involves setting aside of reserved grazing land or enclosure developed in response to acute fodder shortages due to drought during dry season (Mlenge, 2002). It existed as early as
1920s (Malcolm, 1953; Brandstrom, 1986; Cited by Rubanza (1999), mainly for fodder supply during dry seasons (Malcolm, 1953), as a strategy to carter for fodder shortages (Issae, 1997). Ngitili on the other hand, is used as a source of thatch grass, fuel wood and poles for houses construction as well as agroforestry intervention
(Otsyina et al., 1997). It is estimated that about 500,000 ha of natural regeneration woodlands had been restored in Shinyanga region of Tanzania between 1986 and
2004 (HASHI, 2004), comprising 350,000 ha of natural woodlands, and 150,000 ha of agroforestry-based interventions. The woodlands have a wide range of socio- economic and ecological values (Monela et al., 2005), as source and sink of the atmospheric CO2 (Zahabu, 2008).
The in situ vegetation conservation system of central regions of Tanzania such as
Shinyanga, Tabora, Singida and even Dodoma region, represents an important vegetation conservation of these fragile ecosystems that play key role for biodiversity conservation (Monela et al., 2005), as well as other environmental conservation benefits.
2 Kishapu district of north-western Tanzania is comprised of about 4,569 ha of natural regeneration woodlands (KDP, 2013). The regenerating secondary forests play important socio-economic and ecological functions including fodder reserve, source of fuel wood, thatch grass, herbal medicinal and natural recreational goals
(Malcolm, 1953; Mlenge, 2002; Monela et al., 2005). However, information is lacking on the performance of the herbaceous layer, shrub and tree components, and soil organic stocks of the Ngitili natural regeneration woodlands of Kishapu district.
Sustainability of the in situ vegetation conservation system is backed by the Sukuma traditional institution (Malcolm, 1953; Rubanza, 1999; Mlenge, 2002; Monela et al.,
2005) as well as the national forest management through participatory forest management (PFM), (URT, 2007). The natural regeneration woodlands in
Shinyanga in most cases are managed jointly through traditional institutions namely,
Sungusungu, Dagashida and Basumba Bataale, under environment committee
(Mlenge, 2002). A number of by-laws are also in place to control wildfires, shifting cultivation and other known agents of deforestation (Mlenge, 2002). The woodlands have received international recognition not only as a means of restoring degraded lands but also for their contribution to atmospheric carbon sequestration both above- ground and below-ground biomass (Monela et al., 2005). About 5 to 15.6 t C/ha, were reported in Ngitili of Meatu, Kahama and Shinyanga rural districts (Monela et al., 2005; Otysina et al., 2008), which are supported by their resilience due to multiple-species.
Natural regeneration woodlands of north-western Tanzania are dominated by Acacia spp., Commiphora spp., Brachystergia spp. and Combretum spp. (Monela et al.,
2005). Dominant grass species include Aristida spp., Themeda spp. and Cynodon spp. Dominant forb species include Triumfetta spp., Monechma spp., Hibiscus spp., 3 Achyranthes spp., Ipomoea spp. and Leonotis spp. Both edaphic and climatic factors have an overriding influence on tree species diversity and tree stocking parameters
(Zahabu, 2008). Plant species diversity has been reported to vary from 1.4 to 3.7 for
Shannon index of diversity (H‟), (Monela et al., 2005).
Herbaceous species productivity varies across sites as well. For instance, Rubanza
(1999), reported a variable range from 0.92 to 1.32 t DM/ha in Meatu district. On the other hand, Kahama district woodlands have high potential of 2.97 to 3.87 t DM/ha
(Issae, 1997), as compared to Meatu district (0.92-1.32 t DM/ha). Tree stocking parameters vary across sites such that tree stocking density ranging from 964 to
3553 stem/ha has been reported (Monela et al., 2005). Tree standing volume has been reported to vary across woodlands (36.04 to 60.5 m3/ha), (Otsyina et al., 2008).
The natural regeneration woodlands conserved under the Sukuma traditional system, could qualify for carbon credits under the current voluntary carbon market schemes
(REDD+ program), (Saha, et al., (2010).
Despite the social and ecological roles of in situ vegetation conservation system of north-western Tanzania, information is lacking on the performance of vegetation species diversity and the associated carbon sequestration potential at specific ecological sites such as Kishapu district. Available information on natural regenerating forests with respect to species diversity and tree stocking parameters such as tree standing density, and biomass productivity is limited to natural woodlands of Kahama, Shinyanga rural and Meatu district (Otysina et al., 2008;
Monela et al., 2005; Ngazi, 2011; Osei, 2014). A study was therefore, carried out to assess the potential of natural regeneration woodlands of Kishapu district in terms of
4 species diversity, carbon sequestration and their contribution on enhanced soil organic carbon stock.
1.2 Statement of the problem
Sukuma in situ vegetation conservation system (Ngitili) has the potential to mitigate atmospheric carbondioxide (CO2) emissions through carbon sequestration (Malcolm,
1953; Brandstrom, 1986), and they form an important carbon sink in the soil that represents climate change mitigation (Zahabu, 2008; Otysina et al., 2008).
Available information on diversity of dominant vegetation species and their associated carbon sequestration potential of natural regeneration woodlands is limited to a study carried in the former Shinyanga region (Monela et al., 2005;
Otsyina et al., 2008). Little is known on the role of natural regeneration woodlands as important carbon sink and biodiversity conservation in specific ecological sites such as Kishapu district. A study was therefore, conducted among three selected natural regeneration woodlands of Nyasamba, Bubinza and Ndoleleji of Kishapu district aimed to determine herbaceous frequency, shrub and tree species diversity, herbaceous biomass productivity, tree stocking parameters and soil organic carbon dynamics.
1.3 Research objectives
1.3.1 General objective
The general objective was to determine vegetation species diversity and carbon stock dynamics of the natural regeneration woodlands of Kishapu district in
Shinyanga region.
5
1.3.2 Specific objectives
Study was guided by the following specific objectives:
i. To determine herbaceous, shrubs and tree species diversity of the natural
regeneration woodlands in the study area;
ii. To quantify herbaceous biomass productivity of the natural regeneration
woodlands in the study area;
iii. To determine the tree stocking parameters (tree standing density, basal area,
standing volume and standing biomass) of the natural regeneration
woodlands in the study area;
iv. To determine above-ground and below-ground vegetation carbon stock of the
natural regeneration woodlands in the study area; and
v. To quantify soil organic carbon stock in the natural regeneration woodlands
in the study area
1.4 Research hypotheses
Study was guided by the following hypotheses:
i. Herbaceous, tree and shrubs species diversity of the natural regeneration
woodlands do not vary across the sites;
ii. Herbaceous species biomass productivity of the natural regeneration
woodlands does not vary across sites;
iii. Natural regeneration woodlands exhibit no variation of tree stocking
parameters in term of tree standing density, basal area, standing volume and
standing biomass across sites;
6 iv. Both above-ground and below- ground carbon stock does not vary across the
natural regeneration woodlands, and;
v. Soil carbon stock do not vary across both sites and depths category in the
natural regeneration woodlands.
1.5 Significance of the study
Results presented in the current study represents an important dissemination package with respect to status and values of natural regeneration woodlands and their contribution on sustainable provision of ecosystem goods and services, specifically carbon sequestration and climate change mitigation. The findings from this study could be used for the determination of carbon base lines assessment of natural regeneration woodlands that would form the basis for payment under REDD+ program in its initiative toward reducing emission from deforestation and forest degradation. The findings from the current work could add value on natural regeneration woodlands for ecosystem enhancement.
The study contributes to institutional awareness with respect to biodiversity, climate regulation, and maintenance of hydrological cycle as well as carbon sink. Data generated in the current work will contribute towards forest-based management policies such as community-based forest management (CBFM) and Joint forest management (JFM) as main forms of participatory forest management (PFM) system. The knowledge on plant species diversity and carbon stocks dynamics of the natural regeneration woodlands in the district could provide an estimate of the amount of CO2 sequestered and mitigated by vegetation. Furthermore, the findings from the study could enrich data to the national as well as global datasets and other environmental practitioners for planning purposes.
7 CHAPTER TWO
LITERATURE REVIEW
2. 1 Theoretical Review
2.1.1 Concepts and definitions
2.1.1.1 Carbon sequestration
Carbon sequestration refers to the process of absorbing carbon dioxide (CO2) from the atmosphere through biological photosynthesis process and depositing it in a reservoir such woodlands/ forests and soils (Magdoff, 1996). The process involves carbon capture and the long-term storage of CO2 as carbon (Sedjo, 2012). Carbon sequestration primarily involves the uptake of atmospheric carbon dioxide (CO2) during photosynthesis and then transfer of fixed C into vegetation (Nair et al., 2011).
2.1.1.2 Forest stocking
Forest stocking refers to a quantitative measure of the area occupied by tree relative to an optimal or desired level of density (Malimbwi et al., 1994). It includes tree stocking density (N) stems per hectare, basal area (G, m2/ha), standing tree volume
(V, m3/ha) and tree biomass (t DM/ha). The National Forestry Resource Assessment and Monitoring (NAFORMA), defines tree stocking density as a measure of how many trees are growing per unit area (NAFORMA, 2015).
2.1.1.3 Forest inventory
Malimbwi et al. (1994), defined forest inventory as the procedure for obtaining information on the quantity and quality of the woodland resources and other characteristics of the land on which the trees and shrubs are growing.
8 2.1.1.4 Forest biomass
Forest biomass is defined as the total mass of living matter within a given habitat or unit of environmental area which is quantified based on forest inventory (Malimbwi et al., 1994).
2.1.1.5 Carbon trade
Carbon trade, is a terminology used to describe, all activities involving bio-carbon under the Kyoto Protocol (Nair et al., 2009), of the United Nations Framework
Convention on Climate Change (UNFCC), it involves international payment of carbon emission credits for REDD that would be made to countries or perhaps provinces/regions.
2.1.1.6 Tree and shrub Species diversity
Wilson (2006), defines tree species diversity as the number of different tree species in a particular area and their relative frequencies.
2.1.1.7 Ngitili
Ngitili refers to an in situ vegetation conservation system, under Sukuma traditional conservation of natural regeneration woodlands, that involves setting aside vegetation as deferred pastures or enclosures developed in response to acute fodder shortages due to drought during dry season (Malcolm, 1953; Issae, 1997; Mlenge,
2002). The Ngitili natural vegetation conservation system existed as early as during the colonial era, around 1920s mainly to carter for acute fodder shortage during the dry season (Malcolm, 1953). Other benefits derived from Ngitili include fuelwood, pole for house construction and thatch grass (Mlenge, 2002).
9 2.1.1.8 Biodiversity
The word biodiversity is used to explain the variety of life on Earth at different levels of biological organization including genes, species and ecosystem (Gaston &
Spicer, 2004), these variation levels of biological organization from genes to species to ecosystems bring about a diverse variability among living organisms. According to United Nations Conversion on Biological Diversity (UN-CBD, 1992), Article 2 defines biodiversity in terms of biological diversity as variability among living organisms from all sources including, inter alia ecosystem, terrestrial ecosystem, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems.
2.1.1.9 Acacia-Commiphora Woodlands
Acacia-Commiphora woodland depicts an eco-region characterised by dominant tree and shrub genera such as Acacia and associated Acacia spp. (A. tortilis, A. millifera,
A. nilotica, A. senegal, A. seyal, A. drepanolobium); genera Commiphora and associated commiphora spp. (C. Africana, C. schimperi, C. edulis, and C. campestris). Other species include Balanites aegyptiaca, Dichrostachys cinerea,
Combretum spp. and Terminalia spp. (Van Breugel et al., 2011). The Acacia-
Commiphora eco-region includes most of the northern circuit National Parks
(Serengeti, Mkomazi, Tarangire and Lake Manyara) and the central part of Tanzania
(Kindt et al., 2011), as well as semi-arid region of central and north western
Tanzania.
10 2.1.2 Theories underlying the study
Study on vegetation species diversity, herbaceous biomass productivity, tree stocking potential as well as soil organic carbon (SOC) was supported by several theories namely, Species-biomass theory (Hawkins et al., 2003), Carbon sink theory
(Falkowski, 2000), and Carbon cycle theory (Deming, 2010).
2.1.2.1 Biomass-species theory
According to Hawkins et al. (2003), biomass productivity is a positive correlated with species richness of any ecological zone. This indicates that an area with potential biomass productivity supporting more plant species richness and the associated carbon sequestration (Brown 1981; Wright 1993). Biomass productivity determine decrease or increase of species richness in any ecosystem (Srivastava et al., 1998).
2.1.2.2 Carbon cycle theory
Carbon cycle theory describe a bio-physical processes that involves absorption of carbondioxide gas (CO2) from the atmosphere by natural processes such as photosynthesis and added back to the atmosphere by other natural processes such as respiration (Deming, 2010). It has two broad carbon movements on the earth. Fast carbon cycle and Slow carbon cycle. The fast carbon cycle refers to the movements of carbon between the environment and living things in the biosphere. A slow carbon cycle on the other hand, involves the movement of carbon between the atmospheres, oceans, soil and rocks.
11 2.1.2.3 Carbon sink theory
Carbon sink theory is a theory that explains the natural phenomenon that accumulates and stores carbon-containing chemical compound for an indefinite period (Falkowski, 2000), by converting more of the CO2 in the atmosphere into the biomass of tree or soil, growing forest, grasslands, and waters of the ocean that represents an important carbon sink.
2.2 Empirical review
The section describes the practical analysis and detailed of information related the study, in such case the analysis focuses on the work titled vegetation species diversity and carbon stock of Ngitili natural regeneration woodlands conservation system of Kishapu district.
2.2.1 Ngitili natural regeneration vegetation conservation system
Ngitili is an in situ Sukuma traditional vegetation conservation system developed as natural vegetation conservation mainly for fodder supply during dry seasons, through setting aside a piece of land as reserved grazing lands or enclosures in response to acute fodder shortages as resulted from drought (Malcolm, 1953).
The system is indigenous to the agropastoral Sukuma ethnic group of central-western
Tanzania (Malcolm, 1953; Otsyina et al., 1997). On the other hand, Ngitili are used as sources of thatch grass, fuel wood and poles for house construction (Otsyina et al., 1997). These traditional mechanisms are important for the management of natural resources as it contributes to soil fertility, restoration and environmental conservation as well as enhanced vegetation regeneration potential. The system is still wide spread in the entire of Shinyanga region, with majority of livestock keeper
12 (Ngendello et al., 1996), and is commonly a practice in Kishapu district (KDP,
2013).
2.2.1.1 Ngitili ownership in Kishapu district
Two forms of Ngitili ownership in Kishapu district and Shinyanga region are recognized, namely, communal Ngitili and private Ngitili. The privately owned
Ngitili include Ngitili owned by groups, and Ngitili owned by institutions such school village governments, faith affiliated groups, and non-governmental organizations (Ngendello et al., 1996), (Table 1).
2.2.1.2 Ngitili natural woodlands vegetation conservation system in Kishapu
district
In Shinyanga region about 600 ha of Ngitili were reported in 1986, and increased to
250,000 ha, in 2001 (Mlenge, 2002; HASHI, 2004).
Table 1: Number, ownership, and size of Ngitili in Kishapu district
Ngitili Area (ha) Ownership Bubinza 385.0 Communal Ndoleleji/Shagihilu 1168.0 Communal Nyasamba 190.3 Communal Ikonda A 55.0 Communal Mwamanota 100.1 Communal Busongo 475.0 Communal Mihama 500.0 Communal Bulima 1675.0 Communal Ndoleleji 20.9 Private
Source: KDP (2013).
By 2004 about 500,000 ha of Ngitili of which 350,000 ha, are natural woodlands and
150,000 ha (HASHI, 2004), are agroforestry have been restored to regenerate 13 naturally in the region (Otsyina et al., 2008; Monela et al., 2005). Similarly, about
4,569.34 ha of Ngitili have been reported in Kishapu district (KDP, 2013), most of which are communally owned under the respective village governments (Table 1).
2.2.1.3 Management of Ngitili natural regeneration vegetation conservation
system of Kishapu district
Like in other parts of Shinyanga region, Ngitili in Kishapu district are managed under traditional Sukuma institutions (Basumba Bataale, Dangashida and
Sungusungu). The Dagashida regulate and control the use of natural resource in the area, as well as protecting individual from accessing resources using traditional guards known as “Sungusungu”, (Mlenge, 2002; Barrow et al., 1992; Maro 1995).
Grazing starts in August/September (extreme dry season), to the end of dry season
(early rainy season) when there is enough vegetation growth to support livestock.
However, variation in season and duration of grazing depend on fodder shortage.
Trespassing and over-grazing by pastoralist has been identified as major challenges facing overall ownership, utilization and management interventions of Ngitili natural regeneration woodlands in the district.
2.2.1.4 Benefits of Ngitili natural regeneration woodlands of Kishapu district
Several values and benefits from Ngitili vegetation conservation system to household and village economies has been reported (Otysina et al., 1997). Ngitili are potentially and significant income source to supplement income from agriculture to diversify people‟s livelihoods and strategies (Monela et al., 2005). The income from
Ngitili are widely used in supporting school fees, diversification of nutrition options (fruits, vegetables, mushroom, edible insects, wild meat), provision of forage
14 for livestock and as a source of herbal medicine, fuel wood, water and place for spiritual activities (Otysina et al., 1997).
On the other hand, Ngitili natural regeneration woodlands vegetation conservation system offers multiple benefit including water catchment, scenic beauty and biodiversity conservation (Otysina et al., 1997; Monela et al., 2005; Otysina et al.,
2008), as well as forest-based ecosystem goods and services such as climate change mitigation through enhanced carbon sequestration.
2.2.2 Land use and management of Kishapu district
Table 2, portrays the main land use types of Kishapu district that include, land set aside for human settlement (19.4 %), recreational activities (6.4 %) and industrial developments (10 %), agriculture (44 %), grazing lands (17 %), forests, woodlands and wildlife conservation (2.2 %), and non-productive land (1.0 %), (URT, 2009).
2.2.3 Herbaceous, shrubs and tree species diversity of Ngitili natural
regeneration woodlands
2.2.3.1 Dominant vegetation species Herbaceous, shrub and tree species has been reported at variable range of domination in the studied Ngitili of Shinyanga region. For instance, about 152 vegetation species were recorded (Monelaet al. (2005), in the selected Ngitili natural regeneration woodlands of Shinyanga region.
15 Table 2: Land use and management of Kishapu district Land use category Area (km2) (%) Industrial area 433.3 10.0 Grazing land 736.6 17.0 Settlement area 840.6 19.4 Agricultural land 1906.5 44.0 Recreation land 277.3 6.4 Forests land 95.3 2.2 Non-productive 43.3 1.0 Total 4333.0 100.0 Land use and management of Kishapu district as of 2013 (KDP, 2013).
Although there was variation in species composition the major vegetation types distinguished were Acacia spp., Brachystergia spp., Combretum spp., Commiphora spp. And Dalbergia spp. Dominant grass species include Aristida spp., Themeda spp., Eragrostis spp., Cynodon spp., Digitaria spp., and Cymbopogon spp.
Dominant forb species include Triumfetta spp., Monechma spp., Hibiscus spp.,
Achyranthes spp., Ipomoea spp., Tephrosia spp. and Leonotis spp. On the other hand, Rubanza et al. (2006), reported relatively high frequencies of Aristida spp.,
Eragrostis spp., Cynodon spp., Dicanthium spp., Cenchrus ciliaris and Panicum spp. in Meatu district of the current Simiyu region (Table 3).
16 Table 3: Dominant herbaceous species of selected natural regeneration woodlands Shinyanga region Grass species Frequencies (%) Aristida spp. 10.8 Cenchrus spp. 7.6 Chloris spp. 6.3 Corchoris spp. 0.6 Cynodon spp. 6.5 Cyperus spp. 0.6 Dactyloctenium aegyptium. 4.3 Digitaria milanjiana. 2.9 Eragrostis spp. 12.5 Heteropogon spp. 0.6 Panicum spp. 1.8 Rhynchelytrum spp. 1.4 Rottboellia exaltata. 9.6 Setaria verticilata. 1.3 Sorghum sudanese 5.4 Spermacose spp. 0.3 Sub-total 72.5 Monechma debile 9.6 Ipomoea spp. 3.8 Chloris spp. 2.9 Sida spp. 6.0 Indigofera spp. 5.2 Monechma debile 9.6 Ipomoea spp. 3.8 Chloris spp. 2.9 Sida spp. 6.0 Indigofera spp. 5.2 Sub-total 27.5 Total 100.0 Source: Rubanza et al. (2006).
17 2.2.3.2 Herbaceous species composition
Herbaceous species composition varies significant between and among Ngitili natural regeneration conservation system (Monela et al., 2005) in Shinyanga region.
A study by Rubanza et al. (2006), reported a variable herbaceous species composition ranging from 0.3 to 10.8 % in Meatu district. The dominant herbaceous species were Cynodon spp. (30.5 %), Aristida spp. (11.4%), Dicanthium spp. (17.8
%), Cenchrus ciliaris (12.9 %), Panicum spp. (11.7%), and Eragrostis spp. (9.2 %),
(Table 3). In addition, Monela et al. (2005), reported Aristida spp., Eragrostis spp.,
Cynodon spp. and Setaria spp. as grass species with relatively high abundance.
Dominant forb species were Leucas stricta, and Monechma debile (Table 4).
2.2.3.3 Tree and shrub species diversity
Tree and shrub species diversity has been recorded at a variable range of selected and studied Ngitili natural regeneration woodlands of Shinyanga (Monela et al.,
2005). Indices of diversity has been reported at variable range. For instance, Monela et al. (2005), recorded index of dominance (C) ranging from 0.04 to 0.29, as well as ranging from 1.8 to 3.7 for Shannon-Wiener Index of Diversity (H‟). Furthermore,
Nuru et al. (2008), reported a diversity (H) of tree and shrub species ranging from
1.9 to 3.7 in Urumwa Forest Reserve (Table 5).
18 Table 4: Herbaceous species composition of selected natural regeneration woodlands of Shinyanga region Grass species Composition (%) Aristida spp. 34.1 Eragrostis spp. 14.5 Cynodon spp. 11.0 Setaria spp. 10.3 Themeda spp. 7.2 Chloris spp. 6.9 Hyparrhenia spp. 4.5 Digitaria spp. 4.1 Cymbopogon spp. 2.1 Sporobolus spp. 2.1 Panicum spp. 1.4 Penisetum spp. 1.4 Ryncheritrum spp. 0.3 Total 100.0 Source: Monela et al. (2005).
2.2.4 Herbaceous biomass productivity of Ngitili natural regeneration
woodlands
Herbaceous biomass productivity portrays a variable range in the reported selected and studied Ngitili of Shinyanga region. For instance, a variable range of 2.97 to
3.87 t DM/ha, was recorded by Issae (1997) in Kahama district. Rubanza et al. (2006), reported a variable range as from 0.92 to 1.32 in Meatu district. The recorded herbaceous biomass productivity was variable across woodlands and is expected to fluctuate with season in the year, the dry season being the most critical one. Therefore, at this level of productivity, Issae (1997) recommended a low stocking rate.
19 2.2.4.1 Factors affecting vegetation species diversity on Ngitili natural
regeneration woodlands
Variable factors have been reported to affect vegetation species diversity in natural regeneration woodlands conserved under traditional in situ vegetation conservation system (Monela et al., 2005; Zahabu, 2008).
2.2.4.1.1 Effect of disturbance on vegetation species diversity of Ngitili natural
regeneration woodlands
Natural disturbance such as fire, wind throw, insect outbreaks, and disease affect species diversity of the sites by changing the vegetation structure, composition, developmental state or growth rate as well as the micro-climatic conditions
(Carmean, 1975), such disturbance would either lead into local species extinction.
20 Table 5: Tree and shrub species diversity of selected Ngitili natural regeneration woodlands Woodlands Shannon Simpson‟s Source index of index of diversity (H‟) diversity (C) Urumwa FR 1.4-3.6 0.04-0.1 Nuru et al. (2008). Kahama district 1-3.7 0.04-0.1 Monela et al. (2005). Shinyanga rural district 1-3.51 0.04-0.1 Monela et al. (2005). Bukombe district 1-3.2 0.08-0.1 Monela et al. (2005). Bariadi district 1-2.8 0.18-0.1 Monela et al. (2005). Maswa district 1-2.5 0.2-0.1 Monela et al. (2005). Meatu district 1-2.2 0.2-0.1 Monela et al. (2005). Shinyanga urban 1-1.874 0.3-0.1 Monela et al. (2005). district
2.2.4.1.2 Effect of grazing pressure on species diversity
Grazing pressure could be associated to land degradation and habitat fragmentation that all together limit species diversity (Janzen, 2004). On the other hand, high grazing pressure on a given piece of land has positively correlated to species diversity reduction (Carmean, 1975). When a portion of the leaf or shoot tissue and even the entry vegetation is removed whether grazed by livestock, wildlife or insects its productivity is lowered, therefore, the ability to support more other species becomes limited.
2.2.4.1.3 Effect of land use practice on species diversity
Land use practices such as burning of vegetation can have a potentially influence on stability of species diversity has been reported to affect species diversity (Kuzyakov et al., 2009). The property most commonly affected is herbaceous layer structure, which is partially responsible for determining species richness and diversity
(Solomon et al., 2007). 21 2.2.5 Tree stocking parameters of Ngitili natural regeneration woodlands
Studies carried out in natural regeneration woodlands of Tanzania revealed variable ranges of stocking potentials, namely, tree stocking density (N, stem/ha), basal area
(G, m2/ha) and standing tree volume (V, m3/ha), (NAFORMA, 2015).
2.2.5.1 Tree stocking density
A variable range of stocking density ranging from 922 to 2543 stem/ha, has been reported across natural woodlands of Shinyanga region (Monela et al., 2005;
Otsyinaet al., 2008; TaTEDO, 2012). For instance, tree standing density ranging from 1053 to 1360 stem/ha, were recorded in Kahama and Shinyanga Rural district
(Otsyinaet al., 2008). Likewise, an average of 455 stems/ha, ranging from 147 to780 stems/ha were reported by Osei (2014), in Kahama and Shinyanga Urban districts.
On the other hand, Monela et al. (2005), reported a variable tree stocking density of
1964 to 3553 stems/ha, in communal owned Ngitili in Shinyanga. Furthermore,
Tanzania Traditional Energy Development Organisation report (TaTEDO, 2012), recorded an average of 922 stems/ha in Shinyanga Urban. Similarly, an average of
1053 stems/ha were reported by NAFORMA (2015), for natural regeneration woodlands of Tanzania (Table 6).
2.2.5.2 Basal area
Studies conducted in selected Ngitili natural regeneration woodlands of central
Tanzania indicated variation between sites with respect to standing basal area
(Philip, 1994). Otsyina et al. (2008), reported a basal area ranging from 11.2 to 16.4 m2/ha in Kahama and Shinyanga Rural district. On the other hand, Monela et al.
(2005), reported a basal area ranging from 3.394 to 5.804 m2/ha, in communal
22 owned Ngitili of shinyanga. Similar report was given by Osei (2014) and TaTEDO
(2012), (Table 6).
2.2.5.3 Tree standing volume
Studies conducted by Otsyina et al. (2008), Monela et al. (2005), and TaTEDO,
(2012), has indicated variable standing tree volume ranging from 6.6 to 60.5 m3/ha, among the selected Ngitili natural regeneration woodlands of Shinyanga region. For instance, Otsyina et al. (2008), reported an average of tree standing volume of 60.5 m3/ha in Kahama and Bukombe district. Similarly, Monela et al. (2005), reported a variable tree standing volume ranging from 6.6 to 27.0 m3/ha in Meatu, and
Shinyanga Rural districts. Furthermore, TaTEDO (2012), reported an average mean of 36.04 in Shinyanga Urban (Table 6).
2.2.6 Tree standing biomass
Variability of tree biomass between Ngitili vegetation conservation system exist
(Osei, 2014). Total carbon stock in the natural regenerating woodlands of
Shinyanga has been reported ranging from 5.1 to 15.6 t/ha (Otsyina et al., 2008).
2.2.6.1 Above-ground biomass
According to Otsyina et al. (2008), variable range of above-ground carbon stock exists. Above-ground biomass (AGB) has reported ranging from 7.6 to 14.19 t/ha of
Ngitili natural regeneration woodlands of Shinyanga. Similarly, a variable range of
1.6 to 6.35 t/ha was reported by Monela et al. (2005) in Shinyanga Rural district. On the other hand, TaTEDO, (2012) reported AGB mean value of 18.0 t/ha, in natural regeneration of Seseko village of Shinyanga Urban district (Table 7).
23 Table 6: Tree stocking parameters (standing density, basal area and standing volume) of selected natural woodlands Woodlands N G V Source (stems/ha) (m2/ha) (m3/ha) Meatu district and 1964-3553 3.4-5.8 6.6-27.1 Monela et al. (2005). Shinyanga rural Kahama district 1053-1360 11.2-16.4 23-60.5 Otsyina et al. (2008).
Shinyanga Urban 922-1200 3.8-8.0 17-48.9 TaTEDO, (2012). Shinyanga rural 147-780 2.3-16.6 Osei (2014). Other natural 860-1053 3.4-8.3 13-55.4 NAFORMA (2015). woodlands
2.2.6.2 Below-ground biomass
Below-ground biomass s (BGB) has been reported to vary differently in various studies (Monela et al., 2005; Otysina et al., 2008). For instance, a study by Otysina et al. (2008), reported BGB ranging from 1.92 to 3.5 t/ha in Kahama and Shinyanga urban districts.
On the other hand, Monela et al. (2005), recorded BGB ranging from 0.4 to 1.59 t/ha in Shinyanga Urban district (Table 7).
24 Table 7: Tree standing biomass of selected natural regeneration woodlands Woodlands Biomass AGB BGB TC (t/ha) Sources (t/ha) (t/ha) (t/ha) Meatu, and 3.3-13.5 1.6 - 6.4 0.4 – 1.6 1.01 -7.9 Monela et Shinyanga al. (2005). Urban district Kahama and 16.3 – 30.2 7.6 – 14.2 1.9 – 3.5 9.5 – 17.7 Otsyina et Bukombe al. (2008). district Shinyanga 23-38.3 7.3-18 2.2-4.5 12.7-22.5 TaTEDO Rural and (2012). Urban
2.2.7 Concentration of soil organic carbon of Ngitili natural regeneration
woodlands
Concentration of soil organic carbon has been reported to vary between woodlands of Shinyanga region. A study by Kimaro et al. (2007), recorded a variable range of
SOC, ranging from 0.2 to 0. 3 kg/m2 in natural woodlands of Shinyanga Rural,
Urban and Kahama districts. On the other hand, Osei (2014), reported a variable range of 0.24 to 0.67 kg/m2 in planted woodlots of Shinyanga Ngitili. A range of
0.11 to 0.17 kg/m2 was recorded by Osei (2014), in natural woodland in Shinyanga
Urban district. A study by Ngazi (2011), recorded a value ranging from 0.62 to 0.9 kg/m2 in Meatu district. Furthermore, TaTEDO (2012), reported variable SOC ranging from 0.1 to 0.9 kg/m2 in Shinyanga Urban district (Table 8).
25 Table 8: Concentration of soil organic carbon (SOC) of Ngitili natural regeneration woodlands Depth SOC Woodlands (cm) (kg/m2) Source Kahama and Shinyanga Urban district 0-30 0.9-0.2 Kimaro et al. (2007). Seseko Village 0-30 0.9-0.6 Ngazi (2011). Sinyanga Rural and Urban district 0-30 0.9-0.1 TaTEDO (2012). Shinyanga Urban and Rural district 0-30 0.7-0.1 Osei (2014). Other natural woodlands of Tanzania 0-30 1.7-1.2 NAFORMA (2015).
2.2.7.1 Factors affecting concentration of soil organic carbon dynamics of
Ngitili natural regeneration woodlands
According to Solomon et al. (2007), variable factors have been reported to affect the amount of carbon stored in a soil. Factors affecting include; soil type, climate conditions, initial content of SOC, depth soil profile, and land management practices.
2.2.7.1.1 Effect of soil depth on concentration of soil organic carbon (SOC) of
Ngitili natural regeneration woodlands
Effect of soil depth on concentration of soil organic matter has been reviewed extensively in the literature (Baldock and Skjemstad, 1999; Solomon et al., 2007).
Concentration of SOC varies widely with soil depth. In general, the deeper the depth the lower the organic carbon. The 30 cm surface of the earth crust is the richest (1.7 kg/m2) layer of organic carbon due to relatively higher organic matter contents ascribed by high rate of vegetation decomposition.
26 2.2.7.1.2 Effect of fire on concentration of soil organic carbon of Ngitili natural
regeneration woodlands
Studies has reported that fire outbreak and burning always result in some loss of nutrients, especially nitrogen, through volatilization or leaching (Pritchett 1979). A major consequence of burning is the reduction or elimination of the surface organic layers of the soil. On the other hand, severity of disturbance, the 'frequency of disturbance, and the time of disturbance plays a significant role on stocking potential of soil (SOC), (Daddow and Warrington, 1983).
2.2.7.1.3 Effect of soil erosion on concentration of soil organic carbon of Ngitili
natural regeneration woodlands
Soil erosion is mainly related to the loss of surface soil organic matter (Lal, 2004), which on the other side reduce the amount of organic matters on the soil, as well as the rate of organic matters decomposition. Eroded soil can be a net sink for or a net source of CO2 depending both on the frame of reference and on the fate of this eroded material (Yoo et al., 2005). Retaining crop residues generally greatly reduces soil erosion and minimizes water losses during fallow periods (Thomas et al., 2007).
2.2.8 Influence of policy and legislation on Ngitili natural vegetation
conservation system
Studies on the role of policy and legislation on conservation has reported that both the current National Forest Policy of 1998 (URT, 1998) and its legalization by the
Forest Act No 14 of 2002 (URT, 2002) recognize the role of community participation in forest management. The general term for this forest management is
Participatory Forest Management (PFM), (URT, 1998; URT, 2006), that emphases community empowerment. Generally PFM is legally supported by the Forest Act
27 No. 14 of 2002 (URT, 2002). PFM applies in two ways which are Joint Forest
Management (JFM) and Community based Forest Management (CBFM). JFM takes places in national forest reserves or some local authority reserves whereas community adjacent to forest are partner in the management (URT, 2006).
2.2.9 Conceptual frame work
Conceptual framework is defined as a network, or a plane of interlinked concepts that together provide a causal-effect relationship between variables for comprehensive understanding of a phenomenon (Andersson et al., 2003).
2.2.9.1 Independent variables
Management of natural regeneration woodlands, silvicultural techniques, deforestation and grazing pressure have been reported as potential influence on stability of species diversity as well as stocking potential (Kuzyakov et al., 2009).
They play a great role on vegetation species diversity and carbon stocking potential
(Frank, 1995; Zahabu 2008). They act as carbon sources and sinks by absorbing as well as emitting carbon by two main basic processes of the carbon cycle which are respiration and photosynthesis.
2.2.9.2 Intermediate variables
Climatic conditions, edaphic factors as well as conservation policies influence vegetation species diversity and carbon stocking potentials (Frank, 1995). High stocking potential of the woodlands depends on management aspects influenced by conservation policies as well as climatic conditions (Solomon et al., 2007). The role of policy and legislation on conservation has recognize the community participation in forest management (URT, 1998; URT, 2002), (Figure 1).
28 2.2.9.3 Dependent variables
Vegetation species diversity, tree stocking potential and soil organic carbon stocks are influenced greatly by independent variables (Figure 1). For instance, grazing pressure could be associated to land degradation and habitat fragmentation that all together limit species diversity (Janzen, 2004). Deforestation has been identified as major challenges facing overall species diversity as well as stocking potential of both tree and soil in a particular ecosystem
29 INDEPENDENT VARIABLES INTERMIDIATE VARIABLES DEPENDENT VARIABLES
1. Management of 1.Conservational 1. Herbaceous Natural i. Policies species regeneration ii. Regulations composition woodlands iii. By-laws 2. Tree and shrub
2. Silvicultural 2. Climate species diversity Techniques 3. Edaphic factors
3. Deforestation 3. Tree stocking 4. Grazing potential pressure 4. Soil stocking potential (SOC)
Source: Current study
Figure 1: The causal-effect relationship
30 CHAPTER THREE
RESEARCH METHODOLOGY
This section describes the research in terms of materials and methods that were employed in the study.
3.1 Study area
3.1.1 Location
The study was conducted in Kishapu district one of the four districts forming
Shinyanga region. Other districts are Kahama, Shinyanga rural and Shinyanga urban. Kishapu district is located in the South-east of Shinyanga region. The district lies between latitude 3º15‟‟S-4º05‟‟S and longitude 31º30‟‟E-34º15‟‟E (URT, 2009).
The district has three administrative divisions; 29 Wards; 117 Villages; 660 sub- villages and one electoral constituent (KDP, 2013). A study on vegetation species diversity and the associated carbon stock was conducted among three selected natural regeneration woodlands namely Nyasamba, Bubinza and Ndoleleji/
Shagihilu of Kishapu district.
The district has an area of about 4,333 km2. About 2% of the area is covered by natural regeneration woodlands; whereby 47% is agricultural land; 18% is grazing land; 5% is settlement; and 28% is gulley and rocky area (URT, 2009). The district is bordered by Meatu and Iramba districts in the East, Shinyanga Rural and
Shinyanga Urban districts in the West, Kwimba and Maswa district in the North, and Igunga district in the South.
31
Source: Current study Figure 2: Map of Kishapu district to show study sites
32 3.1.2 Climate
Kishapu district is characterized by a dry tropical climate with temperature ranging from 22 0C to 30 0C and 15 0C to 18.3 0C for maximum and minimum, respectively.
The district receives mean annual rainfall ranging from 450 mm to 990 mm per annum (NBS, 2013). Rains starts in late October or early November and usually unreliable and unevenly distributed and end in April/May (NBS, 2013).
3.1.3 Edaphic characteristics
According to KDP (2013), Kishapu district is characterized by flat and gently undulating plains covered with low and sparse vegetation. Soils in the district are described differently based on variation of relief features such that on hilltops soils are moderately well drained greyish brown and sandy (ferric acrisols and oxisols),
(KDP, 2013). On the low -lying bottom lands soil are moderately deep well drained, greyish brown sand loams (ferric luvisols) locally known as “Ibushi” as clay loam soil. Mostly the district is dominated by poorly drained black cotton soil (cambisols and vertisols) locally termed as “Mbuga” (Ngendello et al., 1996).
3.1.4 Vegetation
Dominant vegetation in Kishapu district include Acacia species such as A. tortilis, A. polyacantha, A. drepanolobium, A. nilotica, A. seyal. and A. senegal (Otysina et al.,
2008). Other tree species found in the district include Adansonia digitata., Balanites aegyptiaca, Albizia spp. and Commiphora spp. (Otysina et al., 2008). A total of
1743.15 ha (38.15%) of the total natural woodlands of Kishapu district (4570 ha) were studied.
33 3.1.5 Demography
According to the National Population Census of 2012 (NBS, 2012), human population of Kishapu district had increased from 239,305 people in 2002 to
272,990 people in 2012 (NBS, 2013). The population include 135,269 (49.5%) males and 137,721 (50.5%) females. The population growth rate for the district had been estimated at 3.9%. Main ethnic groups living in the district include Sukuma,
Nyiramba, Nyisanzu and Taturu (KDP, 2013).
3.1.6 Socio-economic activities
Residents of Kishapu districts are agropastoralists. The major economic activities in the district include crop farming and livestock keeping that together form about 88
% of the total economic activities, followed by forestry and fishing (6.3%), mining
(0.90%), petty trade (0.90%) and others (3.9%), (KDP, 2013). About 90% of the population live in the rural areas and practice agropastoralism. Cotton is the main cash crops while sorghum and maize are the staple crops. Subsistence crops include paddy rice, sweet potatoes, cassava, beans, finger millets and groundnuts are cultivated.
3.2 Research design
A quantitative research was adopted. Complete Randomized Block Design (CRBD) was employed in the study with the assumption that more factors causing variation existed between study sites. Concentric circular plots of 15 m with inner sub-plots of
2, 5 and 10 m radii were established as blocks. Kothari (2004) defines a research design as the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure. In fact, the research design is the conceptual structure within which
34 research is conducted, it constitutes the blueprint for the collection, measurement and analysis of data. As such the design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.
3.2.1 Sampling design
Systematic sampling design was employed in this study whereby temporally concentric circular sample plots of 2 m (0.0013 ha), 5 m (0.007857 ha), 10 m
(0.031429 ha) and 15 m (0.0707 ha) were laid out systematically as described by
Malimbwi et al. (1994) and as detailed by NAFORMA (2010; NAFORMA, 2015).
The inter-plot and inter transect distance were maintained at an interval of 300 m and 550 m, respectively. Philip, (1994) stated that systematic sampling design ensured an even distribution of the samples throughout the woodland area and thus increase the chances of inclusion of all vegetation types in the study. In each plot, tree numbers, stem numbers in case of forked trees and Dbh were enumerated. The boundary of the woodlands was visited and coordinates were marked and mapped using GPS.
The generated coordinates were used to trace the sample plots. A local botanist was used in identification of tree species. For specimens that proved difficulty to identify in the field, the samples were collected for further identification at the school of Biological Science, the Department of Conservation Biology of the
University of Dodoma Tanzania.
35 3.2.2 Study population
The targeted population in the study was the natural regeneration woodlands of
Kishapu district in Shinyanga region. Kothari (2004) defined the term „population‟ refers to the total of items about which information is desired.
3.2.3 Sampling frame
A sampling frame in the study was the natural regeneration woodlands of which three natural regeneration woodlands were selected as representative of the district namely Bubinza (385 ha), Ndoleleji/ Shagihilu (1168 ha) and Nyasamba (190.3 ha) with a total area of 1743.3 ha (38.15%) in Kishapu district (4570 ha).
3.2.4 Sampling unit
Temporally concentric circular plots of 5 m (0.007857 ha), 10 m (0.031429 ha) and
15 m (0.0707 ha), respectively, were the sampling unit in this study as described by
Zahabu (2008) and detailed by NAFORMA (2010).
3.2.5 Sampling intensity
This study adopted a sampling intensity of 0.3% which is equivalent to 74 sample plots. The sampling intensity in this study was low. Financial status, time limitation and purpose of the forest inventory necessitate the sampling intensity to be low
(Malimbwi et al., 2005).
3.2.6 Sample size
Sample size was determined using the equation as adopted by Munishi (2004):
n = (TA*Si)/ (PS*100%).
Where, n = maximum number of sample plots, TA = Total area of the study, Si = sampling intensity, PS = plot size.
36 A total of 74 temporal sample plots were established at an interval of 300 m and eleven transect lines were established at an interval of 550 m in study area (Figure
3).
3.2.7 Measurable parameters
The parameters that were measured in each plot included herbaceous diversity in terms of frequencies and percentage composition, shrub and tree species diversity as well as herbaceous productivity. Tree stocking parameters that were assessed included standing density, basal area, standing volume and standing biomass. In addition, measurement such as Diameter at breast height (Dbh), Height and Soil organic carbon were measured.
3.3 Data collection techniques
Different forest inventory biophysical data collection techniques were adopted to determine vegetation species diversity, quantify the amount of carbon of both vegetation species and soil carbon stocks in the natural regeneration woodlands.
Forest inventory was preceded by a reconnaissance survey that involved establishing transects and plot laying-out on the map of the woodlands (Figure 3).
3.3.1 Herbaceous species composition
Herbaceous species diversity was determined from herbaceous species composition.
Herbaceous species composition was established using a 0.5 by 0.5 m (0.25 m2) metal quadrat (Crowder and Chheda, 1982). The quadrat was thrown systematically in each of the four quarters of the innermost circle within the 2 m radius (Plate 1).
On each plot herbaceous species composition were visually estimated using a point sampling technique. Thus, herbaceous species diversity was subjectively assessed based on frequencies of the individual species (Rubanza et al., 2006). Basal cover 37 was visually estimated via subjective scoring of bare ground against vegetation cover and recorded in percentage.
3.3.2 Shrub and tree species diversity
Shrubs and trees indices diversity were determined from shrub and trees tally within a concentric circular plot in the following manner; within a circular plot of 15 m radius, all trees with Dbh ≥ 1cm were tallied as trees.
Figure 3a: Plots distribution of selected natural regeneration woodlands of Ndoleleji/ Shagihilu of Kishapu district
38 All trees with Dbh ˂ 1cm were tallied as shrubs within 5 m radius. Both trees and shrubs were recorded, counted and identified based on their nomenclature for both vernacular and botanical as described by (Abramsky et al., 1993; NAFORMA,
2010).
Figure 3b: Plots distribution of selected natural regeneration woodlands of Shagihilu in Kishapu district
39
Plate 1: Field herbaceous species visual estimation on thrown quadrat
3.3.3 Herbaceous biomass productivity
Herbaceous biomass productivity was determined from the herbage harvested from the previously laid out circular plots for vegetation assessment. Grasses and forbs were cut at 2 cm above the ground using hand sickles, and immediately transferred to a pre-weighed labelled paper bags (Crowder and Chheda, 1982; Rubanza et al.,
2006). The cut herbage was instantly weighted in the field for fresh weight determination using a sensitive weighing balance (±0.001g accuracy). Sub-samples
40 from each quadrat were re-weighed into a separate paper bags before being transported to the laboratory for drying. The sample were dried in a forced air oven at 60°C for 48 hours to constant weight. Dry matter (DM) was computed as:
DM (%) = Weight of oven dry sample *100 Flesh weight sample (FWT)
The herbaceous biomass productivity was expressed in DM. The DM yield productivity (t DM/ha) was determined according to the formulae:
Herbaceous biomass productivity (t DM/ha) = Average DM yield *10000m2
0.25m2
3.3.4 Tree stocking parameters
Forest stand parameters that were studied included, tree stocking density (stems/ha), basal area (G, m2/ha), tree standing volume (V, m3/ha), and tree standing biomass (t
DM/ha). Computation of parameters from tree measurements were done in the following manner: within 5 m radius; all trees with Dbh ≥ 2 cm and < 5 cm were recorded; within 10 m radius; all trees with Dbh ≥ 5 cm and < 20 cm were recorded; within 15 m radius; all trees with Dbh ≥ 20 cm were recorded (Figure 4).
41
R1 R2 R3 R4
Where; R1=2 m, R2= 5 m, R3= 10 m and R4= 15 m radii. Figure 4: Circular plot showing sub-plots for measurement of tree stocking parameters Source: Zahabu (2008)
Three trees (small, medium and large size) were selected in each plot and measured for stump diameter at 30 cm diameter at breast height (Dbh) using diameter tree calliper. Tree height was measured using Suunto hypsometer as described by
Malimbwi et al. (1994) and detailed by Zahabu (2008).
3.3.4.1 Tree stocking density
The number of stems per unit area (stems/ha) determined using tree tally and recorded. Number of stems or tree were counted and recorded in each plot and expressed on hectare (ha) basis.
3.3.4.2 Tree basal area
Tree basal area (G, m2/ha) was determined from a formula of an area (πd2/4).
Diameter at breast height (cm), was measured at 1.3 m above the ground (Plate 2).
42 Thus, computation of the basal area (m2) was attained based on the equation as described by Malimbwi et al. (1994).
2 th Tree basal area = πd /4 = 0.0000785*Dbhi .
Where: π =3.142857, Dbhith =diameter at breast height for the ith tree (cm).
3.3.4.3 Standing tree volume
A single tree volume equation: V=f*g*h (Malimbwi et al., 1994) was used to compute the volume of each tree; where g is the tree basal area at breast height (m2), h is the tree height and f is the form factor of 0.5.
3.3.5.1 Above-ground biomass
Above-ground tree biomass (AGB) was computed based on the equation:
(NAFORMA, 2010).
AGB (t/ha) = Tree stem volume (m3/ha) * Tree density (kg/m3)/1000
Where: tree density = 500 kg/m3 which is equivalent to 0.5 t/m3.
43
Plate 2: Measurement of tree Dbh using tree diameter caliper at Bubinza woodland
3.3.5.2 Below-ground biomass
Below-ground biomass (BGB) was determined as a function of AGB (Kuyah et al.,
2012).
BGB (t DM/ha) = AGB * 0.25
Under this approach, BGB was determined as the product of AGB which was computed by multiplying the AGB value with 0.25 as described by Kaonga and
Bayliss-Smith, 2009).
44 3.3.5.3 Tree carbon stock
The obtained results of both AGB and BGB were converted into tree carbon using the „Default carbon conversion factor‟ of 0.47 as detailed by NAFORMA (2010).
3.3.6 Quantification of soil organic carbon stock
Soil samples for bulky density was determined across a pit of (1.2 m x 1.2 m x 1 m) which was dug at the centre of the plot. Iron core samplers of 5 cm diameter and 5 cm height (volume = 98.214cm3) were used to estimate soil organic carbon stocks
(SOC) in the woodlands as described by NAFORMA (2010). The collected soil samples were transferred into a pre-weighed labelled bag. The samples were instantly weighed in the field to determine fresh weight (FWT) using 0.001 g precision weighing balance.
3.3.6.1 Determination of Bulky density
Soil samples for bulky density (BD) were collected at five levels depths; 0-20 cm,
20-40 cm, 40-60 cm, 60-80 cm and 80-100 cm (Plate 3). Soil samples were bulked by plot by depth by transect and by sites to get one composite sample for each depth category. The soil samples were packed in a well labelled paper bags and shipped to the laboratory for analyses. The soil samples were oven drying at 1050 C for 48 hours.
The BD was then, computed as a ratio of the oven dry weight to soil core volume for each sample and expressed in g/cm3
Bulky density= Oven dry weight sample (g) Soil core volume (cm3)
Bulk density was used to convert soil volumes to soil mass within a given area and soil depth (Kuyah et al., 2012).
45 3.3.6.2 Concentration of organic carbon determination
Organic carbon content (% OC) was determined by Walkley-Black wet oxidation method of Walkely-Black (Nelson and Sommers, 1982), (Plate 4). The percentage
OC was converted to SOC concentrations based on the formula (NAFORMA,
2010):
SOC (kg/m2) = (V* BD *%OC) /1000
where: V = Volume (cm3). % OC = percentage of organic carbon content. BD =
Bulk density (g/cm3)
Soil organic carbon (SOC) was determined from three volumetric soil samples collected from three strata (0 -10 cm; 10 – 20 cm; and 20 – 30 cm) depth categories using auger (Plate 4). The soil samples were air dried, ground to pass through 2 mm mesh sieve using a mortar and pestle.
46
Plate 3: Pits preparation for soil Plate 4: Soil sampling using auger at sampling for bulky density Ndoleleji/Shagihilu at Bubinza woodland woodland
3.4 Statistical analyses
3.4.1 Herbaceous species composition
Data on herbaceous species composition (frequencies and composition) were computed using microsoft excel computer program. Data were analysed for mean and standard error using the General Linear Model Procedure (GLMP) of SAS
Statistical package (SAS, 1999) based on the model:
Yi = μ+ Ri +ei
th where: Yi is the i general response on herbaceous species composition; μ is general mean peculiar to an individual herbaceous species; Ri is the effect of sites on
47 observed herbaceous species composition, and ei is a random error term common for each observation.
3.4.2 Shrubs and tree species diversity
Data on shrub and tree species diversity were computed using microsoft excel computer program to determine index of dominance (C=Pi2) and Shannon-Wiener index of diversity (H‟=Pi*ln Pi), (Krebs, 1989). The data were analysed for mean and standard error by using General Linear Model Procedure (GLMP) of SAS
Statistical package (SAS, 1999) based on statistical model:
Yi = μ+ Ri +ei
th whereby: Yi is the i general response on shrub and tree species diversity; μ is general mean peculiar to an individual species; Ri is the effect of sites on observed shrub and tree species diversity and ei is a random error term common for each observation.
3.4.3 Trees stocking parameters
Data on tree standing density (stems/ha), basal area (G, m2/ha), tree standing volume
(V, m3/ha) and tree standing biomass (t DM/ha) were computed using microsoft excel computer program. The data on tree standing parameters were computed for mean and standard error using GLMP of SAS Statistical package (SAS, 1999).
Analysis of variance (ANOVA) was carried out based on the model:
Yi = μ+ Ri +ei
th where: Yi is the i general response on trees stocking parameters; μ is general mean peculiar for an individual tree species; Ri is the effect of sites on observed tree stocking parameters and ei is an error term common for observation.
48 3.4.4 Soil organic carbon
Soil samples were dried and titrated in the laboratory (Plate 5), organized in excel and computed for means and standard error using the General Linear Model
Procedure (GLMP) of SAS (SAS, 1999) based on the model:
Yij = μ+ Ri + Dj + (R*D) ij + eij
th whereby Yij is the i general responses on the interaction of sites and depth on organic carbon (OC); μ is general mean peculiar to an individual sites and depth category; Ri is effect of site on observed OC, Dj is the effect of depth on observed
SOC; (R*D) ij is the effect of sites and depths on observed SOC and eij is an error term common for each observation.
Plate 5: Preparation of soil samples for oven dry and laboratory titration to quantify concentration of SOC
49 3.3.6 Validity and reliability
Validity and reliability address issues concerning to the quality of data an appropriateness of the methods used in carrying out the research project (Kothari,
2004).
3.4.5.1 Validity
Validity refers to the degree to which an instrument measures what it is supported to measure (Kothari, 2004). In the current work different strategies were used to ensure validity of the study included preliminary survey and pre-testing of data collection tools. In addition, duplicates and triplicates of samples during data analysis where applicable was employed. Clear set up of the experimental procedures to match the objectives were considered during laboratory works.
3.4.5.2 Reliability
Reliability refers to the extent to which results are consistent overtime an accurate representation of the total population understudy (Robson, 2002). It is concerned with making sure that the methods of data collection lead to consistent results. In the current work various technique were employed to ensure reliability of the study, such that data collection tools were pre-tested (trial administration of an instrument to identify flaws), (Polit and Hungler, 1995). Similarly, tools were constructed very carefully based on the theories and objectives of the study and frequently reviewed during data collection as well as verification of findings being certain.
50 CHAPTER FOUR
RESULTS
4.1 Herbaceous, shrubs and tree diversity
4.1.1 Dominant vegetation species of Ngitili natural regeneration woodlands of
Kishapu district
Results on the noted dominant herbaceous species in Kishapu district were variable across woodlands (P< 0.05). The current work revealed different vegetation species dominating the woodlands. A total of 78 vegetation species were identified in the natural regeneration woodlands of Kishapu district. Dominant grass species were
Aristida spp., Cyperus spp., Cynodon spp., Sporobolus spicatus and Cenchrus spp.
(Table 9). Dominant Forb species were Monechma debile., Commelina spp.,
Amaranthus spp., Corchorus capsularis., Abelmoschus esculentus., and Cucumis spp. (Table 10). Dominant tree and shrub species are indicated in Table 11. Tree constituted 58.2% while shrubs occupied 41.8% in the selected natural regeneration woodlands of the district. Dominant shrub and tree species were Acacia spp.,
Dichrostachys spp., Euphobia spp., Commiphora spp., Balanite aegyptiaca., and
Combretum obovatum.
4.1.2 Herbaceous species composition of Ngitili natural regeneration woodlands
of Kishapu district
Herbaceous species composition was variable (p<0.05) across sites. Results on herbaceous species composition are presented on Table 12. Grass species were more diverse compared to forb species in the selected natural regeneration woodlands of
Kishapu district.
51 Table 9: Dominant grass species of selected natural regeneration woodlands of Kishapu district Scientific name Common name Aristida spp. Wire grass Bothriocloa insclupta Silver bluestem Branchiaria spp. Buffalo grass Cenchrus spp. African foxtail grass Chloris gayana. Tumble wind mill grass Chloris barbata Swollen finger grass Cynodon dactylon Bermuda grass Cyperus esculentus Yellow nut sedge Dactylectenium spp. Crows foot grass Digitaria scalarum Couch grass/Hairy crab grass Echnochloa spp. Eragrostis curvula Weeping love grass Heteropogon spp. Spear grass Hibiscus trionum Flower of an hour Panicum trichocladum Switch grass Rhyncherytru spp. Red top grass Rottboellia exaltata Guinea fowl grass Setaria verticillata Love grass Sorghum spp. Sporoborus spp. Sand drop seed Themada spp. Red oat grass Unidentified grass species
52 Table 10: Dominant forb species of selected natural regeneration woodlands of Kishapu district Scientific name Common Vernacular Abelmoschus esculentus Okra Bamia Achyranthes spp. Agave sisalana Sisal Katani Agricus spp. Aloe vera Aloe Amaranthus spp. Pig weed/ Wild amaranth Makangwanzoka Cassia occidentalis Chamaecrista rotundifolia Cleome gynandra Spider Plant/ Mgagani Clitoria spp. Atlantic pigeonwing Commelina spp. Benghal dayflower Convolvulus spp. Kidney weed Upuna Corchorus capsularis Jute mallow Mlenda Cucumis spp. Cucumber Limbe Indigofera spp. Creeping indigo Ipomoea spp. Yams Marando saji Leucas martinicensis Bobbin weeds Zunzu Lycopersicon lycopersicum. Tomato Nyanya Monechma debile Yinza Mamaye O- Oxygonum sinuatum Double thorn shogolo Sida spp. Solanum incanum Sodom apple Spermacoce spp. Sphaeranthus suaveolens Hardheads Tragia brevipes Datura stramonium Thorn apple Tribulus terrestris Punching Vine Shogoro Sonchus luxurians Home Lettuce Lusunga
53 Table 11: Dominant tree and shrub species of selected natural regeneration woodlands of Kishapu district Botanical name Common name Vernacular Acacia drepanolobium Whistling thorn Ilula Acacia nilotica Red thorn acacia Mhale Acacia polyacantha Falcon's claw acacia Nguu Acacia Senegal Igwata Acacia seyal black thorn acacia Idubilo Acacia tortilis Igunga Adasonia digitate Ng'wandu Albizia Harvey sickle-leaved albizia Mpogolo Azadirachta indica Mwarobaini Balanites aegyptiaca Nyuguyu Capparis tomentosa Lubisu Cassia abbreviate long-pod cassia Nunda lunda Colotropis procera Ipambula Combretum obovatum large-fruited combretum Igobheko Commiphora Africana Ntinje Dichrostachys cinerea Chinese lantern tree Itundulu Diospyros spp. Persimmon tree Isubhata Euphorbia supina Milk purslane Lonzwe manga Euphorbia hexagon Six- angle euphobia Nangale Euphorbia tirucalli Rubber hedge Inala Grewia bicolor false brandybush Bukoma Leucaena leucocephala White popinac Lusina Senna siamea Nsongoma Senna singueana Ntungululu Tamarindus indica Nshishi Ormocarpum trichocarpum Ilula mbuli Unidentified species 1 Lunjula Unidentified species 2 Inallo
54 Table 12: Herbaceous species composition (%) of selected natural woodlands of Kishapu district Grass species Composition (%) Aristida spp. 28.9 Cynodon spp. 12.9 Dactylectenium spp. 6.1 Echnochloa spp. 4.2 Cenchrus spp. 3.2 Cyperus spp. 2.7 Branchiaria spp. 2.1 Rhyncherytru spp. 1.9 Sporoborus spp. 1.8 Unidentified grass sp.1 1.5 Setaria spp. 1.5 Themada spp. 0.9 Digitaria spp. 0.8 Paniucum spp. 0.6 Chloris spp. 0.6 Heteropogon spp. 0.5 Chloris gayana. 0.2 Roettboellia spp. 0.2 Sorghum spp. 0.1 Sub-total 70.7 Forb species Monechma debile 4.6 Leucas spp. 3.5 Commelina spp. 3.3 Ipomoea spp. 2.3 Sida spp. 2.3 Oxygonum sinuatum 2.3 Amaranthus spp. 2.2 Lycopersicon spp. 1.7 Tragia brevipes 1.5
55 Cucumis spp. 1.1 Chamaecrista rotundifolia 0.1 Corchorus capsularis 0.8 Sphaeranthus spp. 0.7 Spermacoce spp. 0.6 Cassia occidentalis 0.5 Abelmoschus esculentus 0.5 Convolvulus spp. 0.5 Ipomoea spp. 0.5 Sub-total 29.3 Total 100.0
Dominant grass species included Aristida spp. (28.93%), and Cynodon spp. (12.9%).
Other grass species with relatively high frequencies were Echnochloa haploclada.
(4.2%), and Dactylectenium giganteum (6.1%) as shown in Table 12. Dominant forb species were Monechma debile. (4.6%), Commelina spp. and Leucas spp. Natural regeneration woodlands of Kishapu district had relatively low vegetative basal cover ranging from 48.3 to 76.11% (p<0.05).
4.1.3 Tree and shrub species diversity of Ngitili natural regeneration woodlands
of Kishapu district
Results on tree and shrub species diversity are presented on Table 13. Bubinza natural regeneration woodland was characterized by relatively higher tree and shrub species diversity ranging from 1.02 to 3.28 for Shannon index of diversity than
Ndoleleji/ Shagihilu natural regeneration woodlands as indicated by lower index of diversity (1.0-1.63). Other studied on natural regeneration woodlands of Kishapu district were characterized by moderate species diversity.
56 4.2 Herbaceous biomass productivity of Ngitili natural regeneration woodlands
of Kishapu district
Results on herbaceous biomass productivity are presented on Table 14. Biomass productivity was variable (P<0.05) across sites. Nyasamba natural regeneration woodlands recorded relatively higher biomass productivity (1.19±0.04 t DM/ha) than Ndoleleji/ Shagihilu natural regeneration woodland (0.92±0.06 t DM/ha).
Bubinza natural regeneration had moderate biomass productivity.
Table 13: Tree and shrub species diversity of selected natural regeneration woodlands of Kishapu district Woodlands Simpson Index of Diversity Shannon Index of Diversity (C) (H’) Nyasamba 0.28 – 0.40 1.07 – 1.31 Bubinza 0.11 – 0.34 1.02 – 3.28 Ndoleleji/Shagihilu 0.22 – 0.48 1.00 – 1.63 Mean 0.20 – 0.41 1.03 – 2.07 Effect of site on * * species diversity a, b Means with the same super scripts along the same column do not significant (p>0.05) differ. * Significantly differ (p<0.05)
57 Table 14: Herbaceous biomass productivity of selected natural regeneration woodlands of Kishapu district Woodlands Herbaceous biomass (t DM/ha) Bubinza 1.10±0.09a Ndoleleji/Shagihilu 0.92±0.06b Nyasamba 1.19±0.04a Mean± SE 1.07±0.06 Effect of site on biomass productivity * a, b Means with the same super scripts along the same column do not significant (p>0.05) differ. * Significantly differ (p<0.05)
4.3 Tree stocking parameters
Results on forest tree stocking parameters were variable (P<0.05) across woodlands,
(Table 15).
4.3.1 Tree stocking density
Results on tree stocking density of natural regeneration woodlands of Kishapu district were variable (P<0.05) across woodlands (Table 15). Nyasamba natural regeneration woodland was characterized by relatively higher number of stems/ha ranging from 1708 to 2298 stems/ha, than Bubinza (823-1219 stems/ha). On the other hand, Ndoleleji/ Shagihilu demonstrated relatively moderate tree stocking density (Table 15).
58 Table 15: Tree stocking parameters (tree stocking density, basal area, tree standing volume, and tree standing biomass) of selected natural regeneration woodland in Kishapu Woodlands Stem/ha Basal Vol (m3/ha) Biomass (m2/ha) (t/ha) Nyasamba 2003±295a 7.6±3.10 a 50.1±19.6 a 29.1±11.3a Bubinza 1021±198b 6.1±2.10 a 41.0±13.1 a 23.8±7.6a Ndoleleji 1510±132ab 7.4±1.40 a 42.56±8.8 a 24.7±5.1a Mean±SE 1521±205 7.0±2.20 44.6±13.8 25.8±8.1 Effect of site on tree * N. S N. S N. S stocking potential a, b Means with the same super scripts along the same column do not significant (p>0.05) differ. NS: Not significant (p>0.05). * Significantly differ (p<0.05)
4.3.2 Basal area
Results on tree basal area is indicated in Table 15. Nyasamba natural regeneration woodland recorded relatively higher tree basal area (7.64±3.10 m2/ha) than Bubinza natural regeneration woodlands (6.11±2.10 m2/ha) and Ndoleleji/ Shagihil (7.4±1.4 m2/ha).
4.3.3 Tree standing volume
Results on forest tree standing volume are similarly presented on Table 15. There was no difference on tree standing volume across and between the selected natural regeneration woodlands (P ˃ 0.05) of Kishapu district. Nyasamba natural regeneration woodland had relatively higher standing tree volume of 50.1±19.6 m3/ha, than Bubinza (41.0±13.1 m3/ha) and Ndoleleji/ Shagihilu (42.5±8.8 m3/ha).
59 4.3.4 Tree standing biomass
Results on tree standing biomass of selected natural regeneration woodlands of
Kishapu district are indicated in Table 15. Nyasamba natural regeneration woodland had a relatively higher standing tree biomass (29.1±11.3t/ha) than Bubinza,
(23.8±7.60 t/ha) and Ndoleleji/ Shagihilu (24.7±5.10 t/ha), respectively.
4.3.5 Tree carbon stock
Results on above-ground carbon stock (AGCs), below-ground carbon stock (BGCs) and total tree carbon stocks (Table 16). There was no difference (P ˃ 0.05) across selected natural regeneration woodlands of Kishapu district. Total tree carbon stocks were similarly across selected natural regeneration woodlands.
60 Table 16: Tree carbon stocks of selected natural regeneration woodland of Kishapu district Woodlands Above-ground Below-ground Total carbon (t/ha) (t/ha) (t/ha) Nyasamba 14.5±5.7 a 3.6±1.4 a 18.2±7.1a Bubinza 11.9±3.8 a 2.9±0.9 a 14.9±4.8 a Ndoleleji 12.4±2.5 a 3.1±0.6 a 15.4±3.2 a Mean±SE 12.9±4.0 3.2±1.0 16.2±5.0 Effect of site on tree N. S N. S N. S carbon stocks a, b Means with the same super scripts along the same column are not significant (p>0.05) differ. NS: Not significant (p>0.05)
4.4 Concentration of soil organic carbon of Ngitili natural regeneration
woodlands of Kishapu district
Results on the concentration of soil organic carbon (SOC) stocks are presented on
Table 17. The SOC was only variable across depths category (P ˂0.05). SOC was not variable between woodlands (P ˃0.05). Nyasamba natural regeneration woodland had relatively higher soil organic carbon ranging from 0.17±0.1 to
0.79±0.01 kg/m2 than Ndoleleji/ Shagihilu (0.10±0.05 to 0.62±0.05 kg/m2).
61 Table 17: Concentration of Soil organic carbon (kg/m2) of selected natural regeneration woodlands across soil depth of Kishapu district Soil depths (cm) Woodlands 0-10 10-20 20-30 Mean ±SE Nyasamba 0.79±0.10a 0.38±0.10b 0.17±0.10c 1.34±0.10 Bubinza 0.68±0.06a 0.30±0.06b 0.08±0.05c 1.06±0.06 Ndoleleji 0.62±0.05a 0.30±0.05b 0.10±0.05c 1.02±0.05 Effect of sites N. S N. S N. S Effect of soil depth * * * Effect of sites*depth * * * a, b Means with the same super scripts along the same column are not significant (p>0.05) differ. NS: Not significant (p>0.05). * Significantly differ (p<0.05).
62 CHAPTER FIVE
DISCUSSION OF THE RESULTS
5.1 Herbaceous, shrubs and tree species diversity of Ngitili natural regeneration
woodlands of Kishapu district
The reported dominant herbaceous, shrub and tree species in the current study denote species that are native to semi-arid and disturbed landscape of East Africa.
Vegetation in general and grass species such as Aristida spp. and Cenchrus spp. are good indicators of aridity and semi-aridity zones of East Africa (Pratt and Gwynne,
1971). The noted indigenous herbaceous species in the current work have similarly been reported to dominate the natural woodlands of Meatu district (Rubanza, 1999) as well as other parts of Shinyanga region (Issae 1997; Monela et al., 2005; Otysina et al., 2008). However, the noted variations in herbaceous species composition both between and within woodlands presented in this work with other studies could be largely ascribed to differences between agro-ecological sites, with respect to both precipitation and soil characteristics as well as management intervention.
Herbaceous species composition depends a lot on grazing pressure. Heavy grazing pressure could result to disappearance of nutrition herbage species (“decreasers”) as well as emergence of less nutritious unpalatable species (“increasers”).
Deforestation and other forms of over exploitation of the tree and shrub resource have been associated with declined shrub and tree species. In situ vegetation conservation for enhanced natural regeneration represents one of the practices of degraded landscapes. Implementation of HASHI/ICRAF afforestation programme in
1986 (Otsyina et al., 1997), has restored a total of 600 ha, and drastically increased to 250,000 ha in 2001 (Mlenge, 2002), that had been degraded due to different forms 63 of over-exploitation, such as the historical overgrazing in the Sukuma land due to over-stocking, deforestation due to the different drivers (social, cultural, energy demand, extraction of fuelwood, and illegal lumbering). All factors associated to deforestation negatively affect species diversity as well as dictate the sustainability of the individual herbs, tree and shrub species. The current work noted that most of
Cynodon spp., Sorghum spp., Digitaria spp., and Rhynchelytrum spp. were found to be dominant in black clay soil locally known as Mbuga. Other grass species such as
Aristida spp., Cenchrus spp., Heteropogon spp., Chloris spp., and Branchiaria spp. were localized in clay loam soil locally known as Ibushi. For instance, Nyasamba natural regeneration woodland is a typical heavy clay vertisol soils (black cotton soil) characterized by high holding capacity and the associated water logging which favours water loving grass species such as Cynodon dactylon. The noted dominant forb species such as Cassia occidentalis., Convolvulus spp., Indigofera spp.,
Oxygonum sinuatum., and Sida spp. clearly denote disturbed soils and could be attributed to certain forms of land degradation due to anthropogenic activities such as overgrazing.
The recorded dominant tree and shrub species such as Acacia spp. (A. nilotica., A. tortilis, A. senegal., A. drepanolobium., A. polyacantha., and A. seyal), as well as other species such as Balanites spp. (desert plum) and even Commiphora spp. represent tree species that are well adapted to arid and semi-arid regions with annual rainfall ranging from about 400 to 800 mm. The noted tree and shrub species have similarly been reported in other parts of Shinyanga and Simiyu regions (Monela et al. (2005); Rubanza et al. (2006); Otsyina et al. (2008). However, the noted variations among authors on the dominant tree and shrub species could highly be
64 attributed to differences between woodlands with respect to edaphic and climatic factors as well as silvicultural management. Enhanced conservation for instance through the traditional in situ vegetation conservation system pave a way for enhanced natural regeneration of secondary regeneration forests (Otsyina et al.,
2008). Silvicultural management for instance through thinning tends to optimize light interception of the understorey vegetation thus enhancing both re-growth and re-seeding of the herbaceous species. However, excessive exploitation of both herbaceous layer and tree components would be reflected through both decreased productivity as well as the number of different species.
5.2 Herbaceous biomass productivity of Ngitili natural regeneration woodlands
of Kishapu district
The noted low herbaceous biomass productivity of 1.07±0.06 t DM/ha of selected natural regeneration woodlands of Kishapu district partly suggest lower side of biomass productivity which could be associated with heavy grazing pressure
(Malcolm, 1953). The noted herbaceous biomass productivity concurs to the previous findings been reported in other parts of the region ranging from 0.92 to
1.32 t DM/ha (Rubanza et al., 2006) in Meatu district. Similarly, Issae (1997), reported herbaceous biomass productivity ranging from 0.92 to 3.87 t DM/ha in
Kahama woodlands. However, the noted variations on herbaceous biomass productivity could be partly explained by management aspects as well as sites specific characteristics.
65 5.3 Tree stocking parameters of Ngitili natural regeneration woodlands of
Kishapu district
The noted high tree stocking parameters in terms of tree stocking density (1521±205 to 2003±295 stems/ha), basal area (6.1±2.1 to 7.6±3.1 m2/ha), standing tree volume
(41.0±13.1 to 50.1±19.6 m3/ha), and standing tree biomass (23.8±7.6 to 29.1±11.3 t/ha), of the current study in Kishapu district depicts typical range of most natural woodlands of north-western and central Tanzania, characterized by semi-arid type climate. Reported findings on tree standing parameters in the current study concur with previous findings in other parts of Shinyanga region. For instance, Monela et al. (2005), reported tree density ranging from 964 to 3553 stems/ha; Otsyina et al.
(2008) recorded a variable tree density ranging from 1053 to 1360 stems/ha, as well as in other parts of central Tanzania such as Urumwa natural forest reserve (Nuru et al., 2008). However, variations in tree standing parameters in the current study could highly be explained by over-exploitation as well as the associated grazing pressure.
5.4 Tree carbon stock of Ngitili natural regeneration woodlands of Kishapu
district
The noted high total carbon stock ranging from 14.9±4.8 to 18.2±7.1 t/ha of the natural regeneration woodlands in the current work partly provide a promising stocking potential of the woodlands for enhanced climate change mitigation and CO2 offset through carbon sequestration thereby, reduce the effects of global warming
(Zahabu, 2008; Otsyina et al., 2008). Thus, suggest that in situ vegetation conservation system would have the potential for enhanced climate change mitigations. Furthermore, the reported findings in the current study concur to
Otsyina et al. (2008), (5.1 to 15.6 t/ha) reported in other districts of Shinyanga
66 region. However, the noted variations to a large extent could be largely associated to variation between sites productivity and management practices.
5.5 Concentration of soil organic carbon of Ngitili natural regeneration
woodlands of Kishapu district
The noted low concentration of SOC (1.14±0.07 kg/m2) in the current work depict lower side of soil stocking potential under Ngitili natural conservation system for enhanced climate change mitigation. Although, the observed relatively higher SOC in Nyasamba of 1.34±0.10 kg/m2 than Ndoleleji/ Shagihilu (1.02±0.05 kg/m2) could be attributed due to specific ecological characteristics (edaphic and precipitation).
The noted higher SOC stocks in the upper most layers between 0 to 30 cm depth
(0.70±0.07 kg/m2) than the lower layers between 30 to 100 cm depths
(0.12±0.05kg/m2) is in line to the general trend of organic carbon (OC) and concur to the fact that OC as well as soil organic matter (OM) tends to decline its concentration with respect to soil depth. The 30 cm upper crust represent a region richer in OC as attributed by high rate of decomposition of plant litter materials
(Issae, 1997; Ngazi, 2011; Kimaro et al., 2007).
Reported low finding on SOC ranging from 0.12 to 0.71 kg/m2 in the current work concur with other studies reported in other parts of Shinyanga region. For instance,
Kimaro et al. (2007), (0.16-0.3 kg/m2), Ngazi, (2011), (0. 6-0.91 kg/m2) and Osei,
(2014), (0.11-0.67). Noted variations on SOC between districts could be attributed by edaphic factors as well as climatic factors. Other factors that could attribute to the noted variations on SOC might be management interventions, grazing pressure and previous history of the land use system (Otsyina et al., 1997; Ngazi, 2011).
67 CHAPTER SIX
CONCLUSION, RECOMMENDATIONS AND AREAS FOR FURTHER
STUDY
6.1 Conclusions
From this study it can be concluded that: 1. Natural regeneration woodlands of Kishapu district were dominated by Aristida
spp. (28.9 %), Cynodon spp. (12.9 %), Dactylectenium spp. (6.1 %), and
Cenchrus spp. (3.2 %), as grass species. Other grass species with relatively high
frequencies included; Cyperus spp., Branchiaria spp., Sporoborus spp.,
Themada spp. and Digitaria spp. Dominant Forb species were Monechma debile
(4.6 %), Commelina spp. (28.9 %), Leucas spp. (2.5 %), Sida spp. (2.3 %), and
Ipomoea spp. (2.3%). Other forb species with relatively high frequencies were
Corchorus capsularis, Abelmoschus esculentus and Cucumis spp. Dominant tree
and shrub species were Acacia spp. (A. nilotica, A. tortilis, A. senegal, A.
drepanolobium, A. polyacantha, and A. seyal). Other dominant tree and shrub
species included; Dichrostachys cinerea and Commiphora spp. Tree species
constituted 58.2%, with respect to 41.8 % of shrub species of the selected natural
regeneration woodlands of Kishapu district.
2. Natural regeneration woodlands of Kishapu district have the potential biomass
productivity ranging from 1.01 to 2.01 t DM/ha, that reflect proper provisions of
fodder (nutritional value) for livestock and wildlife species.
68 3. There are promising tree stocking potentials ranging from 6.1 to 11.9 t C/ha
between natural regeneration woodlands of Kishapu district that demonstrate an
important mitigation measures on climate change to offset atmospheric CO2
produced by different drivers including anthropogenic activities that could rise
the effects of global warming.
4. Ngitili natural regeneration vegetation conservation system is a valuable practice
for vegetation species diversity and the associated carbon sequestration potential
for enhanced climate change mitigation.
6.2 Recommendations
From this study it can be recommended as follow:
1. Farmers in Kishapu district should reduce utilization and improve the
management of forest-based resources through protecting and conserving the
available plant species (in situ), as well as introducing new plant species (ex situ)
with high regeneration potential in the woodlands;
2. There is a need for the communities living around Ngitili natural regeneration
woodlands to adopt afforestation project for improving their quick regeneration;
3. To avoid low herbaceous biomass productivity reported in this study, there
should be change of grazing options through differed grazing for enhanced re-
growth and re-seeding of herbaceous species in the wet seasons; and
69 4. To maintain the promising carbon stocking potential reported in this study there
should be re-establishment of many more woodlands and less depending on
resources generated from the natural regeneration woodlands.
6.3 Areas for further study
Specific studies on natural regeneration woodlands of Kishapu district suggested
by this study:
1. Assessment of community perception on agroforestry system such as rotational
woodlots and fodder banks practices for reducing grazing pressure over natural
woodlands.
2. Forest inventory to identify shrub and tree species likely to disappear as the
result of over-exploitation.
70 REFERENCES Abramsky, Z. & Rosenzweig, M.L. (1993). How are Diversity and Productivity Related? In: R.E. Ricklefs and D. Schluter (Eds); Species Diversity in Ecological Communities. University of Chicago Press. Arizona: 52–65.
Andersson A., Hallberg, N., & Timpka, T. (2003). Model for Interpreting Work and Information Management in Process-Oriented Healthcare Organizations, International Journal of Medical Informatics, U.S. National Library of Medicine, Vol.72, no. 3: 47–56.
Ajayi, O. C., Place, F., Kwesiga, F. and Mafongoya, P. (2005). The Impact of Natural Resources Management Technologies on small-scale farmers: Case of fertilizer tree fallows in Zambia. Working paper no. 5. Nairobi: World Agroforestry Project, Chipata: 55.
Albrecht, A., and Kandji, S. T. (2003). Review: Carbon sequestration in tropical agroforestry systems. Journal of Agriculture, Ecosystems and Environment, International Centre for Research in Agroforestry (ICRAF), Nairobi, Kenya, Elsevier Science Publsher (doi:10.1016/S0167-8809(03)00138-5). Vol. 99, no 5: 15-27
Baldock, J.A., Skjemstad, J.O. (1999). Organic soil Carbon/Soil organic Matter. In: K.I. Prveril, L.A. Sparrow. and D. J. Reuter (Eds.); Soil Analysis: An Interpretation Manual. CSIRO Publishing Press, Victoria; Australia: 159-170.
Baldock, J.A. (2007). Composition and Cycling of Organic Carbon in soil. In: P. Marschner and Z. Rengel, Z. (Eds.); Nutrient Cycling in Terrestrial Ecosystems. Springer Verlag, Berlin Heidelberg: 1-35.
Barrow, E. G. Fry, C. P. and Lugeye, S. (1992). Hifadhi ardhi Shinyanga (HASHI) Evaluation Report for Ministryof Tourism, Natural Resources and Environment: United Republic of Tanzania and Norwegian Agency for International Development, Dar-es-Salaam, Tanzania: 34-40.
Barrow, E. and Mlenge, W. (2003). Trees as key to Pastoralist Risk Management in Arid Landscapes in Shinyanga, Tanzania and Turkana, Kenya. International Conference on Rural Livelihoods, Forest and Biodiversity. Held in Bonn, German (2003): 35.
Bellassen V., Manlay R. J., Chéry J. P., Gitz V., Touré A. Bernoux M., and Chotte J L. (2010). Multi-criteria Spatialization of Soil Organic Carbon and Sequestration Potential from Agricultural Intensification in Senegal. Springer Science + Business Media B.V. (DOI 10.1007/s10584-009-9635-x), 98: 213– 243.
Brandstrom, P.E.R (1985), The agro-pastoral Dilemma: Under-utilization or Over- utilization of land among Sukuma of Tanzania. Working papers in Africa studies no. 8. African studies programme. Department of cultural Anthropology, University of UPPSALA, Sweden: 39-42. 71 Brown, J.H. (1981). The Two decades of Homage to Santa-Rosalia; Toward a General Theory of Diversity. Journal of American Zoologist, Vol. 21, no 3: 877–888.
Burke, I.C.Y., Yonker, C.M., Parton, W.J., Cole, C.V., Flach, K., and Schimel, D.S. (1989). The Texture, climate, and cultivation effects on soil organic matter content in U.S. Grassland soils. Soil Science Society of America Journal. Vol.53, no 3: 800-805.
Burgess, N.D; Bahane, B; Clairs, T. (2010). „Getting ready for REDD+ in Tanzania: A case study of progress and challenges.‟ Oryx Publisher, Qatar. 44: 339– 351.
Bernoux M., De Carvalho M., Volkoff., B. and Cerri C. C. (2002). Brazil‟s Soil Carbon Stocks. Soil Science Society of America Journal. Vol. 66, no 3: 888- 896.
Carmean, W.H. (1975). Forest Site Quality Evaluation in the United States. Advances in Agronomy, Elsevier Publishing Unit. Vol. 27: 209-269.
Chamshama, S.A.O., Mugasha, A.G. and Zahabu, E. (2004). The Biomass and Volume Estimation for Miombo Woodlands at Kitulangalo, Morogoro,Tanzania. Southern African Forestry Journal 20: 49-60.
Crowder, L.V. and H.R. Chheda. (1982). Tropical Grasslands Husbandry. Longman, London: 554-562.
CurielYuste J, Baldocchi D.D, Gershenson A, Goldstein A, Misson L, Wong S. (2007). Microbial Soil Respiration, its Dependency on Carbon inputs, soil temperature and moisture. Department of Environmental Science, Policy, and Management, University of California, Berkeley, Global Change Biology (2007) 13,1–18. (doi: 10.1111/j.1365-2486.2007.01415).
Daddow, R.L.; Warrington, G.E. (1983). The Growth limiting soil bulk densities as influenced by soil texture. Fort Collins Company Ltd, U.S. Department of Agriculture, Forest Service. Watershed Systems Development Group: 17.
Dalal, R.C., Mayer, R.J. (1986). The Long-term trends in fertility of soils under continuous cultivation and cereal cropping in southern Queensland. Australian Journal of Soil Research. 24:281-292.
Dejene, A., Shishira, E.K., Yanda, P.Z. & Johnsen, F.H. (1997). Land Degradation in Tanzania: Perception from the Village. World Bank Technical Paper No. 370. Washington DC: 26-29.
Deming, A. (2010). "King of the elements?". Nanotechnology. doi:10.1088/0957- 4484/21/30/300201. (Retrieved 15 November 2017).
72 Elverfeldt, C. S., Schwarze, S. and Zeller, M. (2008). Could carbon payments be a solution to deforestation? Empirical evidence from Indonesia 12th Congress of the European Association of Agricultural Economists (EAAE), Held in 2008: 443.
Falkowski, P.K; Scholes, R.J; Boyle, E; Canadell, J; Canfield, D; Elser, J; Gruber, N; Hibbard, K; et al. (2000). The Global Carbon Cycle: Test of Our Knowledge on Earth as a System. Journal of Science., U.S. National Library of Medicine, U.S.A: 290 (5490): 291–296.
FAO. (2010). Global Forest Resources Assessment: Main report. Food and Agriculture Organization of the United Nations. Forestry Paper no. 163. Food and Agriculture Organisation, Rome, Italy: 340-343.
Frank J. and Millero, (1995). Thermodynamics of carbon dioxide system in the oceans and Ocean Acidification. A National Strategy to Meet the Challenges of Changing Ocean.Washington, DC: National Academies Press, Washington: 321.
Gaston, K.J & Spicer, J.I (2004). Biodiversity: An introduction. 2nd Edition. Blackwell Blackwell Science Ltd, Blackwell Publishing company Carlton, Victoria, Australia: 12-14.
Giliba, R.A., Boon, E.K., Kayombo, C.J., Musamba, E.B., Kasindye, A.M., Shayo, P.F. (2011). Species Composition, Richness and Diversity in Miombo woodland of Bereku Forest Reserve, Tanzania Forestry Training Institute, Olmotonyi, Tanzania: 24-26.
Gower, S. (2001). Production of terrestrial ecosystems. In: H.A. Mooney (Ed); Encyclopedia of Global Change. Chichester, Blackwell Science Ltd, UK: 46, 76-77.
Gower, S. (2003). Patterns and Mechanisms of the Forest Carbon Cycle. Annual Review of Environment and Resources, Department of Forest Ecology and Management, University of Wisconsin, Madison, Wisconsin, 28: 169-204.
Gower, S. and Ahl, D. (2006). The Carbon and greenhouse gas budgets for Wisconsin forests and forest product chains. Wisconsin Department of Energy: Madison, Wisconsin: 67.
HASHI-ICRAF (2004). HASHI -ICRAF Agroforestry Research Project, Shinyanga, Tanzania: 2004 annual report. HASHI -ICRAF Shinyanga, Tanzania: 61.
Hatibu, H. M. S. (2010). Contribution of dryland forests to Rural household livelihoods in Kishapu District, Shinyanga Region, Tanzania. Dissertation for Award of Degree Master of Science at Sokoine University of Agriculture, Morogoro, Tanzania: 146.
73 Hawkins, B.A., Field, R., Cornell, H.V., Currie, D.J., Gue´gan, J.-F., Kaufman, D.M. (2003). An Energy, Water, and Broad-Scale Geographic Patterns of Species Richness. Journal of Ecology, Vol. 84, no 6: 3105–3117.
Howard, E., Gower, S., Foley, J., Kucharik, C. (2004). The Effects of logging on carbon dynamics of a jack pine forest in Saskatchewan, Canada. Global Change Biology, 10: 1267-1284.
Husch, B., Miller, C.I. and Beers, T.W. (1982). Forest Mensuration, New York [htt://www.cnx.org.content/m1217] Retrieved 17 November 2017): 12-26.
Issae, I.M. (1997). Evaluation of Fodder Productivity and Grazing Management Strategies on Traditional Fodder Reserves “Ngitili” in Shinyanga Region. ICRAF Report, ICRAF-Tanzania Agroforestry project, Shinyanga, Tanzania: 34-53.
Janzen, H. H. (2004). Review: On Carbon Cycling in Earth systems a soil science perspective. Journal of Agriculture Ecosystem and Environment. Published by Elsevier B.V, Lethbridge, Alta., Canada. 104: 399–417.
Jobbagy E.G, Jackson R.B (2000). Vertical Distribution of Soil Organic Carbon, its Relation to Climate and Vegetation. Ecological Applications. Department of Botany, Duke University, Durham, North Carolina, USA: 10(2), 2000, pp. 423– 436.
Kaisi, M. M., Xinhua, Y. and Licht, M. A. (2005). Soil C and N changes as affected by tillage system and crop biomass in a corn-soy bean rotation. Applied Soil Ecology, Vol. 30, no 6:174-191.
Kaonga, M. and Bayliss-Smith T. P. (2009). Carbon Pools in Tree biomass and Soil in Improved fallows in Eastern Zambia. Journal of Agroforestry System. Springer Science + Business Media, British, Vol.76, no 1: 37-51.
Kilahama, F. B. (1994). The Indigenous Technical Knowledge: A Vital Tool for Rural Extension Strategies. A Case Study from Shinyanga Region, Tanzania. Forest Trees and People Newsletter 24:30-35.
Kimaro, A. A., V. R. Timmer, S. A. O. Chamshama, A. G. Mugasha, and D. A. Kimaro. (2007). The Nutrient use efficiency and biomass production of tree species for rotational woodlot systems in semi-arid Morogoro, Tanzania. Journal of Agroforestry and System. Springer Science Business Media B.V. Publisher, Vol. 71: no 4: 175-184.
Kindt, R., Breugel, P., Lillesø, J.-P. B., Bingham, M., Sebsebe Demissew, Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H. Mulumba, J., Namaganda, M., Ndangalasi, H.J., Ruffo, C. K., Jamnadass, R. and Graudal, L. (2011). Potential Natural Vegetation of Eastern Africa. The description and tree species composition for bushland and thicket potential natural vegetation types. Forest and Landscape Working Paper (4):64-2011.
74 Kishapu District Profile, (2013). District Development Director, Shinyanga: 37-40.
Kothari, C.R. (2004). Research Methodology: Methods and Techniques. New Age International (P) Ltd. New Delhi: 401.
Krebs CJ (1989). Ecological Methodology. New York: Hamper Collins Publishers: 654.
Kuyah S., Muthuri C., Jamnadass R., Mwangi P., Coe R., Neufeldt H., (2012). Allometric Equations for Estimating Biomass in Agricultural Landscapes. Journal of Agriculture, Ecosystems and Environment. Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya, 158 (2012) :216– 224.
Lehmann J (2007). A handful of carbon. Nature. Department of Crop and Soil Sciences, Cornell University, Ithaca, New York, USA: Vol. 447: 143-144.
MacDicken, K. (1997). A Guide on Monitoring Carbon Storage in Forestry and Agroforestry Projects. Report No. 1611. Winrock International Institute for Agricultural Development, Forest Carbon Monitoring Program and Development, Arlington, USA: 87.
Malimbwi RE (1997). Fundamentals of Forest Mensuration. Morogoro: Department of Forest Mensuration and Management, Morogoro, Tanzania: 36-47.
Malimbwi, R.E., Solberg, B. and Luoga, E. (1994). Estimation of Biomass and Volume in Miombo Woodlands at Kitulang‟alo Forest Reserve, Tanzania. Journal of Tropical Forest Science 7 (2): 230-242.
Maro, R. S. (1995). In situ Conservation system of Natural Vegetation for Sustainable Production in Agro-Pastoral Systems. A Case Study of Shinyanga, Tanzania. Centre for International Environment and Development Studies, Noragric. Agriculture; University of Norway, Aas: 67-69.
Malcolm, D. W. (1953). The Sukumaland, An African People and Their Country: A Study of Land Use in Tanzania. Oxford University Press, London 13-27.
Mlenge, W. (2002). Revival of customary landscape in Shinyanga Region, Tanzania Forests, Trees and People 45: 21-28.
Mlenge W. (2004). NGITILI: The indigenous natural resources management system in Shinyanga, Kenya: Arid Lands Information Network-Eastern Africa (ALIN-EA): 56.
Monela, G.C, Chamshama, S.A.O, Mwaipopo, R. & Gamassa, D.M. (2005). A Study on the Social Economic and Environmental Impacts of Forests Landscape Restoration in Shinyanga Region, Tanzania. Final report: Ministry of Natural Resources and Tourism Forestry and beekeeping division and IUCN – the World Conservation Union Eastern Africa regional office: 23-34. 75 Munishi, P.K.T. and Shear T.H. (2004). The Carbon Storage in Afromontane Rain Forests of the Eastern Arc Mountains of Tanzania: Their Net Contribution to Atmospheric Carbon. Journal of Tropical Forest Science 16(1):78-93.
NAFORMA (2015). National Forestry Resources Assessment Study: Mapping Land Cover Changes using Landsat, Automatic Classification and Visual interpretation. Tanzania Forest Services Agency. Dar es Salaam, Tanzania: 96.
NAFORMA, (2010). Field Manual Biophysical Survey. Ministry of Natural Resources and Tourism. Tanzania Forest Services Agency. Dar es Salaam, Tanzania: 21-30.
Nair, P. K. R., Kumar, B. M. and Nair, V. D. (2009). Agroforestry as a Strategy for Carbon Sequestration. Journal of Plant Nutrition and Soil Science © Springer Science +Business Media B.V, Netherlands: 172: 10-23.
Nair, P.K.R. and Kumar, B.M. (2011). Carbon Sequestration Potential of Agroforestry Systems: Opportunities and Challenges, Advances in Agroforestry, © Springer Science +Business Media B.V. 2011, Netherlands: 8:129–143.
National Bureau of Statistics Tanzania (2013). A 2002 Census Population Distribution as per 2012 Population Census Administrative Units report. Dar es Salaam, Tanzania: 127.
Nelson, D.W. and L.E. Sommers, (1982). The Total carbon, organic carbon and organic matter: In: (A.L. Page, R.H. Miller and D.R. Keeney) Methods of soil analysis. Part 2 Chemical and Microbiological Properties, Open access academic publisher, Woldia University, Woldia, pp: 539-579.
NGAZI, H.S (2011). The potential of Rotational Woodlot and Ngitiri systems in Carbon Sequestration and Soil fertility Improvement in Meatu district, Tanzania. Thesis submitted in fulfilment for the requirements of the degree Doctor of Philosophy of Sokoine University of Agriculture. Morogoro, Tanzania: 16-24
Nuru, H., Rubanza, C.D.K. and Nezia, C.B. (2008). Governance of Key players at district and village levels on health improvement of Urumwa Forest Reserves, Tabora: Ten years of JFM, In Proceeding of the first Participatory Forest Management (PFM), Research workshop: Participatory Forest Management for Improved Forest Quality, Livelihood and Governance: 13 -26.
Nyadzi, G. I., R. M. Otsyina, F. M. Banzi, S. S. Bakengesa, B. M. Gama, L. Mbwambo and D. Asenga. 2003. Rotational woodlot technology in north- western Tanzania: tree species and crop performance; Journal of Agroforestry System Tumbi Agricultural Research Institute, Tabora, Tanzania, Vol.59, Issue 3, pp 253–263.
76 Nyadzi, G. I., Oduol, P. A. and Matakala, P. (2006a). Wood biomass, nutrient accumulation and C sequestration in rotational woodlots agroforestry system in West Tanzania. In: S.A.O Chamshama et al. (Ed); Proceedings of the 2nd National Agroforestry and Environment workshop by 12-16 July 2006, Mbeya, Tanzania: 171-180.
Nyadzi, G. I., Janssen, B. H. and Oenema, O. (2006b). Analysis on the effect of rotational woodlots on nutrition and maize yield following trees in Western Tanzania. Journal of Agricultural Ecosystem and Environment, 116: 93-103.
Osei, A. K. (2014). Estimating Carbon Stocks in Tree Biomass and Soils under Rotational Woodlots and Ngitili systems in North-western Tanzania. Dissertation submitted for the award of Master Degree of Science at the University of Saskatchewan Saskatoon, Canada: 62-67.
Otsyina, R. M., I. Issae and D. Asenga. (1997). Traditional grassland and fodder Management systems in Tanzania and potential for improvement, Proceedings of the international grassland congress, Held in Saskatoon, Ottawa, Canada, March, 1997: 67
Otsyina, R., Rubanza, C. and Zahabu. E (2008). Contribution of Tree Planting and Conservation Activities to Carbon Offsets in Shinyanga; Dar es Salaam. Tanzania. Royal Norwegian Embassy: 13.
Philip, M.S. (1994). Measuring Trees and Forests; 2nd (Ed). Centre for Agriculture and Bioscience (CAB) International, Wallingford, UK. 310.
Phelps, J; Webb E.L: Agrawal, A. (2010). Does REDD+ Threaten to Recentralize Forest Governance? Journal of Science, Published by AAAS, Netherlands 328: 312–313.
Polit, D.F and Hungler, B.P (1995). Nursing research, principles and Methods; 6th
(Ed). Lippincott William and Wilkins Publishers, New York: 38.
Pratt, D. J. and Gwyne, M. D. (1971). Rangelands Management and Ecology in East Africa, Krieger Publishing Company, Huntington, New York: 310.
Pritchett, W.L. (1979). Properties and management of forest soils. John Wiley and Sons Publishing Company, New York: 500.
Robson, C (2002). Real World Research, a resource for social scientist and practitioners‟ researchers; 2nd (Ed). Blackwell Publisher, London: 49.
Rubanza, C. D. K. Shem, M. N. Ichinohe, T. & Fujihara, T. (2006). Biomass Production and Nutritive Potential of Conserved Forages in Silvopastoral Traditional Fodder Banks (Ngitiri) of Meatu District of Tanzania: Asian- Australian Journal of Animal Science. Tanzania Forestry Research Institute (TAFORI), Shinyanga, Tanzania Vol. 19, no. 7: 978 – 983.
77 Rubanza, C. D. K. (1999). Effect of Leucaena leucocephala (Leucaena) leaf meal supplementation on the growth performance of cattle grazing traditionally conserved forages (Ngitiri). Dissertation submitted for award of Master Degree of Science, Department of Animal Science and Production, Sokoine University of Agriculture, Morogoro, Tanzania: 134.
Saha S., Nair P.K.R, Nair V.D, Kumar, B.M (2010). The Carbon Storage in Relation to Soil size-fractions under Tropical tree-based land-use systems. Journal of Plant Soil, Kluwer Academic Publishers. Netherlands Vol. 328, no. 5:433– 446.
SAS (1990). SAS User‟s Guide: Statistical Analytical System (Version 5); 5th (Ed). SAS Inc, Cary, N.C. The University of Georgia Press: 69.
Sedjo, R; Sohngen, B. (2012). "Carbon Sequestration in Forests and Soils". Annual Review of Resource Economics. Annual Reviews: 127–144 (Retrieved on September 9, 2017).
Skutsch, M (2010). The Community Forest and Monitoring for the Carbon Market: Opportunities under REDD. London, UK: Earthscan: 234.
Solomon S, and Manning M, D (2007). Technical Summary. In: S, Q Solomon, M. D Manning, et al; Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, United Kingdom and New York, USA: 79-87.
Srivastava D.S. & Lawton, J.H. (1998). Why Most Productive Sites have More Species: An experimental test of theory using tree-hole communities. Journal of American Naturalistic, 152: 510–529.
TaTEDO (2012). Pilot Project on Community-Based REDD+ Mechanism for Sustainable Forest Management in Semi-Arid Areas (Case of Ngitili in Shinyanga region), Semi-Annual progress report, July-December 2011: Dar es salaam, Tanzania: 14-19.
UN-CBD (1992). United Nations Conversion on Biological Diversity, International Convention on the Conservation of Biological Diversity held in June 12, 1992, in Brazil: 89.
UNFCC (2008). Coping Strategies Data Base: Ngitiri in Shinyanga, Tanzania”. In: A. Agrawal. Sweeney, M.C. and N. Perrin, (2008); Local Institutions and Climate Change Adaptation. The Social Dimension of Climate Change Report No. 113.
URT (1998). United Republic of Tanzania National Forest Policy. Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division, Dar es Salaam, Tanzania: 59.
78 URT (2001). National Report on the Implementation of the Convention on Biological Diversity report: Division of Environment Vice President's office, Dar es salaam. Tanzania: 12-23.
URT (2009). Kishapu District Profile, District Development Director, Shinyanga, Tanzania: 37.
URT (2001), United Republic of Tanzania Project Implementation Manual: Annex 2. Improving Service Delivery for Participatory Forest Management. Report. Ministry of Natural Resources and Tourism, Forestry and Beekeeping Division, Government Printer, Dar es Salaam, Tanzania: 403.
URT (2002). Forest Act. Ministry of Natural Resource and Tourism, Government Printer, Dar es Salaam, Tanzania: 147-164.
URT (2002). Forest Act. Forestry and Beekeeping Division, Ministry of Natural Resources and Tourism, Government Printer, Dar es Salaam, Tanzania: 1281.
URT (2003). Framework for Participatory Forest Management. Ministry of Natural Resources and Tourism. Government Printer, Dar es Salaam, Tanzania: 238.
URT (2006). Participatory Forest Management in Tanzania-Facts and Figures. Ministry of Natural Resource and Tourism, Government Printer, Dar es Salaam, Tanzania: 119.
Van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H. N., Namaganda, M., Ndangalasi, H.J., Ruffo, C. K., Jamnadass, R. and Graudal, L. (2011). Potential natural vegetation of eastern-Africa: Description and tree species composition for bushland and thicket potential natural vegetation types. Forest and Landscape Working Paper 64: 4. 111.
Waide, R., Willig, M.R., Steiner, C.F., Mittelbach, G., Gough, L., Dodson, S.I., Juday, G.P. and Parmenter, R. (1999). The Relationship between Productivity and Species Richness. Annual Review of Ecology and Systematics, Springer- Verlag, New York: 30: 257–263.
Walkley A. and Black I.A., (1934). The Examination of Degtjareff Method for Determining soil organic matter and a proposed modification of the chromic acid titration method. Journal of Soil Science, Vol. 37, no 6: 29-38.
Walter, H. (1973). The Vegetation of the earth in relation to climate and the Eco- physiological conditions. The English University Press, Ltd; New York, London: 237.
Wilson, E.O. (2006). Threat to Global Biodiversity; [http://www.globalchange] doi:10.1186/1750-0680-2-3. (Retrieved on 16 April, 2017)
Wright, D.H. (1983). Species–Energy Theory: An extension of Species– Area theory. Chicago Press, Chicago. Oikos, 41: 496–506. 79 Wright, D.H., Currie, D.J. & Maurer, B.A. (1993). The Energy Supply and Patterns of Species Richness on Local and Regional Scales. In: R. E. Ricklefs and D. Schluter (eds); Species Diversity in Ecological Communities. Historical and Geographic Perspectives. University of Chicago Press, Chicago: 66–74.
WWF (1999). An Exploring Biodiversity: A guide for Educators around the World. 2nd (Ed); World Wildlife Fund-US and Conservation International, Washington DC: 84.
Yoo, K., Amundson, R., Heimsath, A.M., and Dietrich, W.E. (2005). Erosion of upland hillslope soil organic Carbon in coupling field measurements with a sediment transport model. Global Biogeochemical Cycles. Agronomy Research Vol 19, no 3:1-17.
Zahabu, E. (2008). A Sinks and Sources: A strategy into Involve Forest Communities in Tanzania. Global climate policy. A Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, University of Twente, Netherlands, 48:52-235.
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