BENEFITS OF REFORESTATION ON CARBON STORAGE AND …

BENEFITS OF REFORESTATION ON

CARBON STORAGE AND WATER

INFILTRATION IN THE CONTEXT OF

CLIMATE MITIGATION IN NORTH

ETHIOPIA

Number of words: 25180

Number of figures: 44

Number of tables: 16

Number of pictures: 11

Jonathan De Deyn Stamnummer: 01410540

Promotor: Prof. dr. Sil Lanckriet, vakgroep Geografie

Copromotor: Dr. Miro Jacob, vakgroep Geografie

Masterproef voorgelegd voor het behalen van de graad master in de richting Geografie

Academiejaar: 2018 – 2019

II

I

FOREWORD

For this masterscription, I collected and analysed data primarily in function of the Ethiotrees restoration

project and secondly to provide new perspectives in the existing literature on carbon sequestration

mechanisms and water infiltration in exclosures in the Dogua Tembien region, Ethiopia. To make this data

collection possible, an intensive two months of fieldwork was required in Ethiopia. For this, I would like to pay

tribute to following persons and organisations. The first thank you goes to the University of Ghent together

with the University of Mekelle and the VLIR-UOS South Initiative. They provided me the opportunity and

financial support to make this research possible. They did not only make sure that I could do this research,

they also believed in me during the complete campaign of fieldwork and data analysis. The second and

biggest thank you goes to my local guide Gebrekidan Mesfin. He showed himself as a very reliable friend

who is not afraid to make his hand dirty or to face long and intensive days in the field. He was more than my

local guide and dragoman, he is also a qualified researcher and the person that gives his very best for you

and the research. He was the most important person during the fieldwork. The next thanks goes to the

numerous daily labours who helped me collecting high qualitive data on the field. Without them, not even half

of the used data would have been collected. From all my daily labours, a special thanks goes to those who I

like to call: “the boys of Maybate”. They were always available as a back-up and were very willing to work

under all kind of circumstances. From these boys, one in particular again needs some special attention:

Gebrmedhin Gereziher, the neighbour of Gebrekidan. He is a young and interested man who joined me on

the field by himself. He has the potential to become an excellent scientist. If I go back to Ethiopia for new

research, I would even consider him as my guide and research partner. The next person to thank is Etefa

Guyassa, my local promotor. He made sure that the cooperation with the university went well and that my

soil samples were analysed in time at the research lab. He was always available and open for discussions

about the research. Arno Moerdijk is the next person who deserves a thank you. Arno and I experienced the

adventure together. He was always in for some jokes and fun but also for more serious discussions about

the research, he gave the complete fieldwork a positive vibe. The last persons to thank in person are my two

promotors Sil Lanckriet and Miro Jacob who are the foundation of this complete set-up. They are the persons

who gave me this outstanding opportunity to combine research with personal development and who were

always willing to control, and if necessary, adjust the research. I couldn’t have asked for better promotors.

I also want to thank all the other persons who supported me during the complete fieldwork/analyses period

and the locals for being so enormously hospitable.

II

III

ABSTRACT / POPULARISERENDE TEKST

In order to tackle the severe threat of land degradation and desertification of the past century in Ethiopia,

communities in Northern Ethiopia have stimulated the establishment of closed areas for biomass

regenerating projects, further named “exclosures”. In this study, we investigate primary the driving factors

behind biomass regeneration and carbon sequestration in exclosures, having a focus on climate, physical

soil, terrain and human driven characteristics, and secondly the effect of exclosures on the infiltration speed

of surface water. We selected 10 exclosures in which we collected data on biomass, driving factors and

permeability; 6 of these exclosures had adjacent grazing land that acted as a “twin catchment” where data

on permeability was also collected. The major driving factor behind carbon sequestration in exclosures is the

linear increase with exclosure duration; the clay content of the soil also has a positive impact on biomass

regeneration. Human interferences showed to be as much a threat as a gift as more isolated exclosures have

higher biomass values and extended management programs results in higher SOC stocks. Twin catchment

analyses on permeability indicated that exclosures have higher surface water infiltration rates than grazing

land and that this is directly correlated to the lowering of the bulk density. Not all the variation in biomass

values could be explained, this illustrates that more components are contributing to the biomass regenerating

process. Our results help authorities to make more thorough location choices when new exclosures need to

be established, we also proved that exclosures are an effective alternative to combat land desertification.

SUMMARY

English

Land degradation and desertification in Ethiopia have repeatedly been reported as a severe threat for the

future of local livelihood (Mekuria et al., 2011; Nyssen et al., 2004; Taddese, 2001). The combination between

high population growth and slow increases in agricultural productivity made sure that new agricultural land

had to be created to satisfy household needs. This new agricultural land is created on steep slopes where

forests were converted to cultivation and grazing land. This land conversion has led to accelerated erosion

and a deterioration of soil nutrients (Mekuria et al., 2007; Mortimore, 1993). In response to these

environmental problems, the communities in North Ethiopia promoted rehabilitation of degraded land by

“closing out” the more marginal areas for reforestation. In these reforested areas, communal grazing and

human interventions are strictly forbidden (Mekuria et al., 2007). The closed areas are named as exclosures

and their objective is to restore the natural resources and vegetation cover on degraded communal grazing

land. It is known that forests are playing an important role in the global carbon dynamics, as they are large

carbon sinks (Vashum & Jayakumar, 2012).

IV

This bring us to this research where we have investigated the dynamics behind the biomass regeneration

process in exclosures, as well as the effect of exclosure on water infiltration rates. A schematic overview

(without references) of the complete research project is visualised in Figure 1: Here, the used methods are

represented as well as potential dynamics behind the carbon sequestration process.

We found out that the biomass regeneration process in exclosures is primarily subject to the uncontrollable

factor “time”. Next to this aspect, other factors are also contributing to an acceleration or delay of the biomass

growth. The soil texture is partly responsible for the inter and intra exclosure variability of biomass. Soils that

contain more clay fractions have the tendency to simulate more biomass regrowth as these soils have higher

cation exchange capacities and water-holding capacities which will result in less nutrient and moisture stress

for the growing vegetation. Soils with more silt fractions generate less biomass regrowth. The other major

driver of carbon sequestration is linked to human impacts. We found that areas that are more isolated from

the human living world have higher biomass values. This indicates that the more accessible areas are more

under the influence of illegal grazing and tree cutting. We also found that exclosures with an expanded

management program result in higher SOC concentrations. We can thus say that human interferences in

exclosures is as much a gift as it is a threat.

On the part of water infiltration, our results proved that exclosures are having higher water infiltration rates

than adjacent grazing land. This higher infiltration rates are directly linked with the lower bulk density values

who are acquired by the exclusion of cattle from exclosures and thus the lack of compaction of the soil by

trampling. No link has been found between SOC concentrations and permeability rates.

Nederlands

Land degradatie en verwoesting in Ethiopië worden in de literatuur herhaaldelijk aangehaald als een ernstige

bedreiging voor de toekomst van het lokale levensonderhoud (Mekuria et al., 2011; Nyssen et al., 2004;

Taddese, 2001). De combinatie tussen de snelle bevolkingstoename en de slechts gematigde toename in

landbouwopbrengsten hebben ervoor gezorgd dat dat nieuwe landbouwgronden ontwikkeld dienden te

worden om te kunnen blijven voldoen aan de huishoudelijke noden. Deze nieuwe landbouwgronden werden

ontwikkeld op steile, marginale hellingen waar bossen werden omgezet naar teelt- en graaslanden. Deze

conversie van het land heeft geleid tot een toename van landdegradatie en erosie alsook tot een afname van

voedingsstoffen in de bodem (Mekuria et al., 2007; Mortimore, 1993). Als reactie op deze milieuproblemen

besloten de gemeenschappen van Noord-Ethiopië om herstelprojecten van gedegradeerd land te promoten

door de marginale gebieden te gaan sluiten voor herbebossing. Op deze herbeboste gebieden zijn

gemeenschappelijke begrazing en menselijk ingrijpen ten strengste verboden (Mekuria et al., 2007). Deze

gesloten gebieden worden “exclosures” genoemd en hun doel is om de natuurlijke hulpbronnen alsook de

vegetatie te herstellen op voorheen gedegradeerd communale graaslanden. Het is bekend dat bossen een

belangrijke rol spelen in de globale koolstof dynamieken doordat ze grote koolstofputten zijn (Vashum &

V

Jayakumar, 2012). Dit brengt ons tot bij ons onderzoek waarbij we enerzijds de dynamieken achter de

generatie van biomassa in exclosures onderzocht hebben, alsook wat het effect is van exclosures op de

infiltratie van oppervlaktewater. Een schematisch overzicht (zonder referenties) van het volledige

onderzoeksproject is gevisualiseerd in Figure 1: Hier zijn de gebruikte methoden weergegeven evenals de

mogelijk verklarende factoren achter het proces van koolstofvastlegging. Dit onderzoek ontdekte dat het

regeneratieproces van biomassa in exclosures, primair onderhevig is aan de oncontroleerbare tijdsfactor.

Naast dit aspect zijn er ook nog andere factoren die bijdragen aan een versnelling of vertraging van de groei

van biomassa. Bodemtextuur is zo een variabele die mede verantwoordelijk is voor inter- en inta-exclosure

variabiliteit van biomassa. Bodems die meer klei bevatten hebben de neiging biomassa groei sterker te gaan

stimuleren doordat deze bodems een betere kationenomwisselingscapaciteit hebben alsook beter in staat

zijn om water vast te houden, hierdoor ondervindt de vegetatie minder gevoeligheid voor nutriënten- en

watertekorten. Bodems die meer leem bevatten produceren lage biomassa waardes. De andere grote

controlerende factor van koolstofvastlegging is de menselijke invloed. We vonden dat gebieden die meer

geïsoleerd zijn van de menselijke leefwereld hogere biomassa waardes produceren. Dit geeft aan dat de

meer toegankelijke exclosures meer onderhevig zijn aan illegale vormen van begrazing en houthakken. Hier

tegenoverstaande vonden we wel dat de exclosures waarvoor er een uitgebreid beheers-programma bestaat

ook hogere concentraties hebben aan bodem organische koolstof. We kunnen dus zeggen dat de menselijke

inmenging in exclosures evenzeer als een geschenk dan als een bedreiging gezien kan worden. Voor het

onderzoek naar waterinfiltratie bewijzen onze resultaten dat exclosures hogere water infiltratie snelheden

hebben dan aangrenzende graaslanden. Deze kunnen toegeschreven worden aan de lagere bulk dichtheid

ten gevolge van de uitsluiting van vee en het dus ‘niet samendrukken’ van de bodem door vertrappeling. Er

werd geen verband gevonden tussen concentraties bodem organische koolstof en de permeabiliteits-

waarden van een gebied.

VI

Figure 1: Schematic overview of the research project

VII

TABLE OF CONTENTS:

1. Introduction ............................................................................................................................................... 1

1.1. Study area ....................................................................................................................................... 3

2. Fieldwork methods ................................................................................................................................... 5

2.1. Carbon sequestration in biomass .................................................................................................... 5

2.1.1. Monitoring biomass (AGB) ...................................................................................................... 5

2.1.2. Measuring belowground biomass (BGB) ............................................................................... 11

2.1.3. Converting biomass to carbon ............................................................................................... 11

2.1.4. Setting up the plots ................................................................................................................ 12

2.1.5. Field techniques for biomass measurement .......................................................................... 14

2.2. Monitoring soil organic carbon ...................................................................................................... 15

2.2.1. Carbon determination method ............................................................................................... 15

2.2.2. Data sampling method ........................................................................................................... 16

2.3. Explaining factors .......................................................................................................................... 17

2.4. Water infiltration ............................................................................................................................. 25

2.4.1. Water mass balance for exclosures ...................................................................................... 25

2.4.2. Measuring groundwater recharge techniques ....................................................................... 26

2.4.3. Soil moisture retention ........................................................................................................... 27

2.4.4. Determination of saturated hydraulic conductivity ................................................................. 28

3. Statistical methods ................................................................................................................................. 29

4. Results ................................................................................................................................................... 31

4.1. Carbon sequestration .................................................................................................................... 31


5. Conclusion .............................................................................................................................................. 62



6. Discussion .............................................................................................................................................. 63

6.1. Method ........................................................................................................................................... 63


6.3. Permeability + bulk density ............................................................................................................ 71

7. References ............................................................................................................................................. 72

VIII

1

1. INTRODUCTION

Land degradation and desertification in Ethiopia have been repeatedly reported as a severe threat for the

future of local livelihood (Mekuria et al., 2011; Nyssen et al., 2004; Taddese, 2001). These two terms can be

defined as ‘degradation of vegetation cover, soil degradation and nutrient depletion’ and ‘the temporary or

permanent lowering of the productive capacity of drylands’ and it is named to be one of the major global

threats to sustainable development (UNEP, 1992). To accentuate this problem, Lal (1995). p.135 reported:

“In many developing countries, annual sediment yields are increasing at a rate equivalent to 1.5 times the

rate of population growth” and according to Brhane & Mekonen (2009), the soil loss on steep cultivated slopes

in Northern highlands of Ethiopia is up to 35t ha-1 y-1, which is twice the amount of the maximum tolerable.

Land degradation in Ethiopia is not caused by a single factor but is due to a more complex interaction between

different components: growing population size, deforestation, soil losses, low vegetation cover and

unbalanced crop production (Taddese, 2001). The utilization of dung and crop residues as an energy source

and fuel to satisfy household energy needs (Fitsum et al., 2000) has disturbed the nutrient cycle to the soil.

This systematic decline of nutrients in the soil resulted in a lower crop yield (Taddese, 2001). The lower crop

yield and increasing population size in combination with low inputs of fertilizer and agricultural machinery

(Taddese, 2001) made sure that new agricultural land had to be created. This new agricultural land is created

on steep slopes where forests were converted to cultivation and grazing land. These more marginal places

were chosen due to the lack of fertile soils in flat lands (Taddese, 2001). This land conversion has led to

accelerated erosion and degradation (Mekuria et al., 2007; Mortimore, 1993). The consequences of erosion

are significant: reduction of crop productivity (Lal,1995), desertification of the area, reduced water infiltration

and water availability, loss of nutrients in the soil and lower biodiversity (Mekuria & Aynekulu, 2011). Initially,

little was done to maintain the vegetation cover after deforestation. “The use of the land as a mine rather

than as a source of renewable sources has led to severe degradation problems” (Taddese, 2001, p. 818).

Another major consequence of land degradation is the transfer of carbon (C) to the atmosphere in the form

of CO2. Van der Werf et al., (2009) noted that deforestation is the second largest anthropogenic source of

carbon dioxide and that it contributes to around 12 percent of man-made carbon dioxide in the atmosphere.

The IPCC, (2007) calculated a contribution of 17 percent while the book of Moutinho & Schwartzman, (2005)

estimates values up to 25 percent. Despite these differences, one cannot deny the importance of

deforestation in anthropogenic greenhouse gas emissions. Reforestation is noted as an effective way to

reduce the increasing CO2 concentrations. Forests sequester carbon from the air into living biomass and the

soil (Mekuria & Veldkamp, 2012) and mitigate climate change. It is a win-win situation, as the sequestered

carbon is able to restore degraded soils, biomass and water infiltration, but it also reduces the rate of increase

of carbon into the atmosphere and counteracts the global heating (Lal, 2004). The fact that the soil is an

important carbon pool is also supported by (Lal, 2004). It is calculated that on a global scale, the net amount

2

of carbon in the soil is three times higher than the net amount of carbon in the atmosphere (Lal, 2004). From

all this soil organic carbon, 73% is stored under forests (Vashum & Jayakumar, 2012).

The total amount of potential carbon sequestration is limited, this limitation is majorly determined by the

specific climatic conditions and the soil profile characteristic (Lal, 2004) (Table 1). The SOC stock, in absolute

numbers, in tropical biomes doubles the SOC stock of temperate biomes. However, tropical SOC stock is

only half the amount of SOC stock in Boreal/Taiga biomes (Lal, 2005).

Table 1: Carbon stocks across different biomes in the world

Source: Lal (2005)

In response to these environmental problems, the communities in North Ethiopia promoted rehabilitation of

degraded land by “closing out” areas for reforestation. It is known that forests are playing an important role

in the global carbon dynamics, as they are large carbon sinks due to the fact that plants are taking carbon

dioxide from the atmosphere and convert it into carbohydrate (Vashum & Jayakumar, 2012). By this, carbon

is stocked in living biomass, or thereafter, as soil organic carbon. In these reforested areas, communal

grazing and human interventions are strictly forbidden (Mekuria et al., 2007). Although, very often, a cut-and-

carry system for controlled grass cutting by associations is allowed (Mekuria et al., 2011). The closed areas

are named as exclosures and their objective is to restore the natural resources and vegetation cover on

degraded communal grazing land. Exclosures are not fenced but protected by guards who are often working

on a food-for-work base (Mekuria et al., 2011). An economic valuation study of exclosures established on

communal grazing lands situated on steep slopes in Tigray has proven that the net economic value of

exclosures is about 28 percent higher than the best alternative land use (Mekuria et al., 2011). In this same

study, the importance of economic surplus for the local communities has been indicated as an essential factor

to obtain the support of the local inhabitants and thus to render the establishment of exclosures successful.

The establishment of exclosures has as reverse effect that the pressure on the remaining communal grazing

land can increase (Mekuria & Veldkamp, 2012). This needs to be taken into consideration when new

exclosures are established.

3

Carbon storage and groundwater recharge

This study is focussing on variation in carbon storage and water infiltration within and between different

exclosures in Dogu’a Tembien, Tigray. The carbon storage in each exclosure can be divided in (i) carbon in

biomass, which includes above ground biomass, belowground biomass, the dead mass of litter and woody

debris, (ii) soil organic carbon (Vashum & Jayakumar, 2012). This carbon storage has economic and

ecological values (Lal, 2004; Mekuria et al., 2011). On the economic aspect, each Mg (=ton) carbon stocked

can potentially be valorised in carbon schemes (Mekuria et al., 2011). The sequestered carbon resulting from

restoration fits in the ecosystem restoration projects. Nevertheless, the accuracy on the results of the amount

of carbon sequestration relies on accurate monitoring, reporting and verification of carbon storage protocols

(Chave et al., 2014). Ecologically, soil organic carbon promotes soil aggregation and the reduction of surface

runoff. It is also a source of energy for soil biota and acts as a temperature regulator (Lal, 2004). This research

tries to find insights into the following questions: (i) What are the driving factors for differences in carbon

sequestration? (ii) Are exclosures having higher water infiltration rates in comparison to adjacent grazing

land and can this be correlated to soil organic carbon?

1.1. Study area

The study area is located in the Dogu’a Tembien district (Figure 2) which is part of the Tigray region in North

Ethiopia and has an area of 1033 km². The geology of the study area is characterized by alternating hard

and soft Antalo limestone which is covered by Amba Aradam sandstone and later on tertiary lava (Nyssen et

al., 2007). The most occurring soil types in this region are Vertisols, Vertic Cambisols, Cumulic Regosols,

Calcaric Regosoils and Phaezoems (Nyssen et al., 2007). The agricultural system in Dogu’a Tembien

consists of small-scale family farms who have mainly 2 or 3 private parcels which are used for cropland and

a combined area of 0.5 to 0.7 ha. Land that is owned by the community is used for grassland, rangeland and

exclosures (Nyssen et al., 2007). The most cultivated crops are barley (Hordeum Vulgare), wheat (Triticum

sp.) and tef (Eragrostis tef) (Mekuria et al., 2007; Nyssen et al., 2007). Each household also owns cattle as

this is part of the permanent upland system (Nyssen et al., 2007).

Meteorologically, the Dogu’a Tembien district shows big fluctuations in precipitation throughout the year.

During the winter, the Intertropical Convergence Zone (ITCZ) is situated to the south of the hemisphere in

Africa. This brings hot winds from the Sahara to the western Highlands which contains almost no rain. From

June on, the ITCZ is situated at its most northern point. This allows south-east monsoons to bring a rain

season to North Ethiopia (Nyssen et al., 2005). This rain season is responsible for more than 80 percent of

the annual precipitation (Nyssen et al., 2007). The interannual rainfall variates between 546 mm in 2002 to

879 mm in 1998 (Nyssen et al., 2005) with an average of 697 mm in Hagere Salem between 1980 and 2014

(NMA). The average monthly minimum temperature fluctuates between 4 and 6°C while the maximal

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temperature ranges from 20 to 22°C (Nyssen et al., 2007). More specific in Hagere Salem, the average mean

temperature ranges between 11 and 15°C between 1980 and 2014 (NMA).

Figure 2: Study area Dogu’a Tembien, Tigray

Source: Writer’s creation

5

2. FIELDWORK METHODS

The fieldwork for the data collection has been carried out in the summer of 2018; more specifically between

the 3th of July and the 29th of August. The fieldwork included a combination of biomass measurements,

collection of disturbed and undisturbed soil samples, measurements of explaining factors for the differences

in carbon content combined with small interviews, and also the standardization of already collected data to

the newly collected datasets. All the data got spatially linked to collected GPS-coordinates using the Garmin

eTrex-30. One overall Excel file was set up which shows all the biomass calculations for each plot in an

exclosure, and also every variable that was collected on the field. The database contains the combination of

all the existing data and the data that were collected by the first author. This file will become fundamental to

the Ethiotrees restoration project.

2.1. Carbon sequestration in biomass

2.1.1. Monitoring biomass (AGB)

In order to start collecting biomass, we first made a literature review on the available techniques. Based on

this literature review, the most desirable technique is selected to be applied in the field.

2.1.1.1. Literature: Above ground biomass (AGB)

Two main techniques are available to measure the above ground biomass, one is the direct measurement in

the field and the other is the use of remote sensing and GIS (Vashum & Jayakumar, 2012). Both techniques

will be discussed in the next paragraph. The advantages and disadvantages of each technique will be taken

into consideration to select the most desirable method to do the research.

Field measurement

The direct field measurement includes two different methods (Vashum & Jayakumar, 2012). The first method

is the harvest method, this method will harvest all trees in the area. The different components of the trees

are oven dried and weighted. The advantage of this method is that the obtained number for biomass, and

thus also carbon, is very accurate. The major disadvantage of the method is the destructive and not durable

aspect. It is also very time consuming, costly and can only be used for small areas (Vashum & Jayakumar,

2012). In the exclosures where this research will be executed is, by the definition of exclosures, harvesting

forbidden. It would damage the rehabilitation project. The harvest method will thus not be used.

The second method is a non-destructive method that estimates the biomass of a tree, based on his physical

parameters. These parameters are the diameter at breast height (DBH), the height of the tree, the volume of

6

the tree and wood density. A combination of these parameters can be substituted into a specie specific

allometric equation to obtain the biomass. It is the most used method to estimate the above ground biomass

(Vashum & Jayakumar, 2012). When site-specific allometric models are not available, a more general model

is required to estimate the biomass (Vashum & Jayakumar, 2012). The advantage of this method is the non-

destructive aspect. Above ground biomass can be measured by using only physical parameters of the trees

and shrubs. The disadvantage of this method is the fact that each specie has its own relationship between

physical parameters and above ground biomass. Physical parameters like the height of the tree may

sometimes be difficult to measure in the field (Vashum & Jayakumar, 2012). To overcome these

disadvantages, more generic allometric models for mixed tree species can be applied and the number of

variables to determine the above ground biomass can be reduced. These implementations will however

cause a higher bias on the results (Vashum & Jayakumar, 2012).

Remote sensing + GIS

With remote sensing, the above ground biomass data of the study area will be collected from a distance. The

data collecting instruments do not need to be in contact with the biomass. The major advantage of this

technique is that it can be used in areas that are difficult to access, it is also the most cost-effective way to

collect data from a large area (Vashum & Jayakumar, 2012). Besides it is time-saving in comparison to the

field measurement techniques, the results are less accurate (Vashum & Jayakumar, 2012). Remote sensing

technique is becoming increasingly used for forest biomass estimation. It does not measure the biomass

directly, it measures the parameters that are correlated to biomass. Allometric equations and ground

calibration for biomass estimation are required to validate remote sensing data (Chave et al., 2014). Different

remote sensing techniques are available (Table 2). The bottleneck of this potential method would not be the

ground calibration but rather the (free) availability of high-resolution data that can measure tree stem

diameters on the centimetre scale. We also desire a technique that is specially developed for regenerating

secondary forests. For these reasons, biomass estimations will not be made with remote sensing techniques.

Table 2: Available remote sensing techniques

Source: Overview based on (Vashum & Jayakumar, 2012)

Technique Reference Notes

Laser profiling data (Nelson et al., 1988)

Landsat TM images (Lu, 2005; Steininger, 2000) More successful for a

successional forest than mature

forest

Lidar (Lefsky et al., 2002; Popescu,

2007)

Penetrate the canopy, single

equation can be used to relate the

remotely sensed canopy

7

structure to the above-ground

biomass. Measures biophysical

parameters of individual trees

High resolution, helicopter-borne

3D scanning Lidar

(Omasa et al., 2003)

Measures the 3D canopy

structure of every tree

Airborne laser scanning (ALS) (Ene et al., 2012) ALS assisted survey is more

efficient than the ground-based

inventory

ALOS AVNIR-2 (optical imagery) (Sarker & Nichol, 2011) Significant improvement of the

biomass estimation

worldview-2 satellite (Eckert, 2012)

Field method: allometric model

An allometric regression model will be used to determine the above ground biomass of exclosures. We will

use a mixed tree species allometric equation instead of the species-specific equation. This because one

hectare of tropical primary forest may contain as much as 300 different species (Chave et al., 2005). This

species diversity is greatly reduced in secondary forests in North Ethiopian where 10 dominant species

accounts for 54% of all individuals (Aerts et al., 2006). The use of species-specific equations would not be

an efficient way of working by two reasons: (i) The chance of a wrong determination of a plant would increase

because not all species are easy to determine. (ii) Not every species has already a specific allometric

equation. As second argument, the species-specific equations will often be applied on a few numbers of

plants while the general allometric equations will more often be a composition of multiple smaller studies and

thus have a larger database to create a model. According to the study of Chave et al. (2014), general

pantropical models do not perform much worse than locally fitted models in terms of model uncertainty. An

important note by the use of these general allometric equations has to be made: Although African tropical

forests count for 30% of the total amount of tropical forest, not a single dataset of African trees is used to

estimate the parameters of general allometric equations (Vieilledent et al., 2012). Later on, with Chave et al.

(2014), the first pantropical models with the introduction of African forest datasets were fitted.

2.1.1.2. Comparison of generic allometric models for above ground biomass estimation

To obtain the most reliable allometric model, a comparison between the characteristic of the different models

(Table 3) and the biomass estimations of the models (Figure 3) is made. It is important to know that not a

single statistical procedure is able to unambiguously decide which model provides the best results (Burnham

& Anderson, 2002). If a model is chosen where wood specific gravity is an explaining parameter, it is

recommended to use a species-level average of wood specific density because direct methods are seldom

8

available (Chave et al., 2005). Chave et al. (2014) have shown that using species-mean wood specific gravity

does not bias the analyses. In cases where tree height is not available, the model of Chave et al. (2014)

outperformed previously published models by incorporating a bioclimatic stress variable E.

The allometric equations provided by Brown (1997) are recommended by the FAO and are widely used for

biomass estimation. These models have the simplest equation, the diameter at breast height (DBH) is the

only independent variable to calculate the AGB. These models have the highest estimations of biomass in

comparison to the other models. The models of Chave et al. (2005) and Chave et al. (2014) are based on

larger databases and cover different tropical forests from all over the world. The models are more complex

than the models of Brown (1997). These models are having three independent variables: diameter at breast

height (DBH); height (H) and specific wood density (p). The ‘specific wood density’-variable may sometimes

be unknown for a given species and may thus provide difficulties estimating AGB. The last model provided

by Mokria et al. (2018) is a model for mixed species in a recovering landscape. The study has been performed

in North-western Ethiopia in an area that has similar characteristics to our study area. The species used to

develop this model are similar to species that are growing in the exclosures of interest. This model is also

specially developed for small trees. This model has the lowest estimations of the above ground biomass in

comparison to the other models.

Table 3: Comparison between different general allometric equations. D stands for diameter at breast height in centimetre; p for wood

specific gravity in grams per cubic meter, H for tree height in meter, BA for the basal area in cm² and AGB for above ground biomass

Source Brown

(1997)

Brown (1997) Chave (2005) Chave (2014) Mokria (2018)

Formula AGB = exp {-

1.996 + 2.32*

ln(D)}

AGB = 10^ {-

0.535+

log10(BA)}

AGB = 0.112*

(pD²H)0.916

AGB = 0.0673 *

(pD²H)0.976

AGB = 0.2451 *

(D230*H)0.7038

Number

of trees

28 191 1808: H and D

2410: D

4004 84

Forest

type

Dry forest

Broadleaf

tree

Dry forest

Broadleaf tree

Dry forest

Broadleaf tree

Dry, moist and wet

forest

Mixed species

Study

area

India Mexico Tropics in Asia,

America, Oceania

Tropic in Asia,

America, Oceania

and Africa

North-western

Ethiopia

Adjusted

r²

0.89 0.94 0.996 0.82

RSE 0.311 0.357: H

0.413: E

AIC 913 3130: H 97.98

9

(Akaike

information

criterion)

4293: E

Overesti

mation

AGB

0.5-6.5%: H + D

5.5 – 16.4%: D

-Individual trees >

30Mg: 20%

underestimation

-Trees 10-30Mg:

2.7%

overestimation

Standard

error

12.5%: D + H

19.5%: D

Range

DBH (cm)

5 -40 cm 3-30 cm 5-156 cm 5-212 cm 2 – 10 cm

Restricti

on

> 900

mm/year

precipitation

< 900 mm/year

precipitation

Status Currently

used at

EthioTrees

(2018)

New formula for

biomass

estimation

Notes -Naturally grown

forests.

-Model I.3, similar

to Brown 1989

-Include the

correction factor

-Mean percent

bias slightly higher

than locally fitted

models

-Only studies

included done by

experienced

ecologists or

foresters

- Separate

equations for dry,

moist and wet

forest do not

improve the

performance of

the model

- Overestimating

of Brown (1997)

by 33.4 %

10

Figure 3: Comparison of biomass estimations by the model of Brown (1997) >900 mm precipitation, Brown (1997) < 900 mm

precipitation, Chave (2005), Chave (2014) and Mokria (2018)

2.1.1.3. Best allometric model

Based on the previous literature review, following formula will be used in the field to determine the above

ground biomass (Mokria et al., 2018) (Equation 1):

AGB = 0.2451 * (D230*H)0.7038 (1)

This model will be used because it is specially developed for small trees in regenerating forests. The study

of Mokria et al. (2018) is also conducted in North Ethiopia on trees species that are similar to the species

that occur in the exclosures. Here, AGB is the above-ground biomass in kilogram per plant, D30 is the trunk

diameter at a height of 30 cm (measured in cm), H is the total tree height (in meter).

The simplicity of the model is a key requirement, the absence of wood specific gravity as a variable is

necessary because not all wood densities of the tree species in the study area are known. This condition

makes that the model provided by Chave et al. (2005) and Chave et al. (2014) will not be applied. Most trees

in the exclosures are having a diameter that ranges between 2 and 8 cm, this implies that the range of DBH

provided by Brown (1997) is too high. Allometric equations should not be used beyond there range of validity

(Vashum & Jayakumar, 2012). The formula of Brown (1997) overestimates the above ground biomass of

small trees in a regenerating forest by 33% while the model of Mokria produces an error ranging from -5.2%

to 8.7% across the different diameter classes (Mokria et al., 2018).

11

2.1.2. Measuring belowground biomass (BGB)

Biomass of trees does not only occur aboveground but is also present belowground in the roots of trees and

shrubs. This root biomass is not as easily measurable as the above ground biomass. One of the most

common techniques to determine the amount of belowground biomass is the use of the root:shoot ratio, this

is the root biomass divided by the shoot biomass (Mokany et al., 2006). This same study made a compilation

of previously published root:shoot ratios to obtain generally applicable root:shoot ratios for the major biomes

across the world. According to their results, the following root:shoot ratio should be applied (Table 4). The

root:shoot ratios found in other literature is also presented in Table 4. Due to the lack on published studies

or reliable datasets for underground biomass estimation in the Tigray (Reubens et al., 2009), a conservative

approach is chosen by taken the lowest, significant value of 0.24 (Cairns et al., 1997). By this, the results will

not be biased in a positive way.

Table 4: Compilation of root:shoot ratios. ND = not defined

Vegetation category Shoot biomass (Mg ha-1) Mean root:shoot ratio Source

Tropical/ subtropical dry

forest/ plantation

<20

>20

0.563

0.275

(Mokany et al., 2006)

Tropical/ subtropical/

temperate dry woodland

ND 0.322 (Mokany et al., 2006)

Tropical zone ND 0.24 (Cairns et al., 1997)

ND 0.27 (IPCC, 2003)

Tigray, Ethiopia ND 0.21 – 0.50 (Not

significant)

(Reubens et al.,

2009)

2.1.3. Converting biomass to carbon

Once the amount of above- and belowground biomass of the vegetation has been found, this value has to

be multiplied with a conversion factor to derive the amount of carbon stored. According to Chave et al. (2005)

and Solomon et al. (2017), it is assumed that 50 percent of the total weight is carbon. The conversion value

of 0.55 is also used (MacDicken, 1997). But again, the lowest value of 0.5 will be taken to not bias the results

in a positive way.

12

2.1.4. Setting up the plots

The biomass measurement plots were determined by adding a grid over the exclosures that follows the

contours. Horizontal transects had a separation of 150 meters while the vertical transects were only divided

by 50 meters. This is similar to the method used by Mekuria et al. (2011). The exact location of each

measuring point was determined by GPS in WGS-84. Each location was also subdivided in 2 compartments

(A and B). In compartment A (20*20m), all trees and shrubs with a circumference >= 6.5 cm (and thus

diameter roughly over 2 cm) and height > 1.5m were recorded and measured. Compartment B (5*5m) is for

all trees and shrubs with a circumference < 6.5 cm or height < 1.5m. This method is based on the method

used by Yami et al. (2006).

Plots are having a theoretical density of one plot every 150 meters in the horizontal dimension and one plot

every 50 meters in the vertical dimension. The centre of the first plot was chosen by visual interpretation on

a place that looks representative for the area. From this point on, the next plot got determined by GPS using

the UPS coordinate system.

The delineation of the 20x20 (and 5x5) plots was done by ropes that have marked knots every 20 (5) meter.

Picture 1 and Picture 2 show how this goes in practical. In picture 1, The blue arrows are showing the four

persons who were standing in the corner holding a marked knot on the rope. The fifth person (here the

cameraman) had to do a visual control on the plot to make sure that there were no clear errors. Once every

corner and centre of the 20x20 and 5x5 plot was established, permanent marker was applied in each corner

on a big rock. This makes sure that in x-amount of years, the exact position of the plot can be found back

and that new biomass measures will not obtain an error due to a shift of the plot. The centre of every plot got

a code with a permanent marker of the form ‘ExTxPx’. Here, the x is the following number, E stands for

Exclosure, T stands for Transect and P for the plot. This same code was used in the GPS to save the location

coordinates. An example of these marks and codes is given in Picture 3; the blue arrows are indicating a

corner while the red arrow indicates the centre.

Picture 1: Setting up a 20x20m plot on the field

Source: Own picture, 17/08/2018

13

Picture 2: Setting up a 5x5m plot on the field


Picture 3: Indication of the corners (blue) and centre (red) of a 5x5 plot on rocks


14

2.1.5. Field techniques for biomass measurement

In the 20x20 plot, every tree and shrub with and circumference > 6.5 cm and a height >1.5m was measured.

For each tree and shrub that fulfilled this condition, we measured the circumference, the height and the

average diameter of the canopy cover. The circumference was measured 30 cm above the ground with the

help of measuring tape, this process is shown in Picture 4. The height got measured with a metal measuring

tape and the applied method was depending on the height of the tree/shrub. One person had to ensure that

the end of the measuring tape was on the same height of the highest point of the tree while the other person

had to read the height at the bottom. In most of the times, when the tree was lower than 2 meter, it was

possible to work the other way around and point the end of the measuring tape at the bottom and to read the

height by yourself. Picture 5 visualises this measuring method: the blue line indicates the height; the double

red arrow runs parallel with the measuring tape. The two persons are indicated by the numbers one and two.

As last, the average diameter of the canopy cover was measured. This was done by measuring the diameter

in the x-axes and y-axes and simply taking the average. Picture 6 shows this process in the field.

Picture 4: Method to measure the

circumference of the tree/shrub


Picture 5: Measuring the height of a tree: the blue

line shows the height, the double red arrow follows the

measuring tape, person 1 is reading the value and person 2

is controlling the height


2

1

15

Picture 6: Measuring the average canopy cover of a tree


2.2. Monitoring soil organic carbon

2.2.1. Carbon determination method

The proportion of carbon in the soil was determined in the laboratory for soil chemistry, Mekelle University,

by using the chromic acid wet oxidation method from Walkley & Black (1934). In this method, the oxidisable

matter in the soil is oxidised by a potassium dichromate solution. This reaction is assisted by a heat

generation when sulfuric acid is mixed with the dichromate. The remaining dichromate is titrated with ferrous

sulphate. The remaining dichromate is inversely related to the proportion of C present in the sample (Walkley

& Black, 1934). This proportion of carbon has to be converted to the soil organic carbon content with the

following formula (Mekuria & Veldkamp, 2012) (Equation 2):

SOC (tonC/ha) = C/100 * Bd * depth(m) * 10.000 m²/ha (2)

In this formula, C stands for the carbon concentration of the soil, depth is the depth of the “black” topsoil and

can be measured using an augering, Bd stands for the bulk density (Mg/m3). For bulk density, the average

value of the exclosure is taken as a generalised value. If this knowledge is not available, the value 1.33 g/cm³

is taken. This is the median bulk density value for Ethiopian topsoil as determined by Girmay et al. (2009).

16

2.2.2. Data sampling method

The SOC monitoring points were determined in the same grid as the above-ground biomass. The position of

every sampling plot was determined by GPS in WGS-84. Each analysed soil sample was a composition of 5

samples (Figure 4). This method is applied, following Mekuria et al., (2011), to avoid a local disturbance in

the proportion of carbon and to obtain a reliable sample that can be used for extrapolation. This also

represents the actual monitoring method of EthioTrees. The samples were taken by augering in the 0-0.2m

depth, literature shows that this top layer contains more than 70% of the total soil organic carbon in forest

ecosystems of the Ethiopian Highlands (Yimer, Ledin, & Abdelkadir, 2006). The determination of the exact

thickness of the topsoil of each sample was done with a tape measure and based on two limitations. The first

limitation is the presence of bedrock before reaching an augering depth of 20cm. Secondly, a visual analysis

was done on the auger sample to find a sharp colour gradient that indicates the transition of the darker topsoil

to the lighter soil. In this case, the topsoil depth is the distance from the gradient line to the surface. This

limitation was, however, difficult to see because the data was collected during the rain season where the

complete soil is very moist and, consequently, darker coloured. This is directly a second reason why the

augering depth was limited to 0.2 meters. If none of these two limitations was applicable when augering, the

soil depth was taken as 0.2 meters.

Figure 4: Soil data sampling method

On the field, the five augering samples were collected and merged together to form one disturbed soil sample.

This sample obtains the same code as the plot. Picture 7 visualizes this process. If a corner consisted of big

in-situ rocks, no samples were taken and the soil depth was considered as zero. In case augering was denied

due to a big rock which was not in situ material, we moved over a few decimeters to retry.

17

Picture 7: Collecting disturbed samples with an auger and measuring its thickness


2.3. Explaining factors

The SOC stock not only gets determined by the major biomes and thus by more climatic driven factors (Lal,

2005) but also by more in situ variations (Jandl et al., 2007; Lal, 2005) and human interferences (Alvarez,

2005) Most studies indicate climatic driven factors as the major determinant for SOC. On the terrain, the

difference in slope, aspect, precipitation (moisture) and altitude are creating different microclimates. These

microclimates will cause variations in temperature, wind dynamics and moisture on a very local scale (Geiger

et al., 1995).

To obtain an insight into our first research question: “What are the explaining factors for differences in carbon

sequestration?”, literature provided different possible factors that were able to collect on the field. Below, we

give a listing of the collected variables and the collection method (Table 5). In the next paragraphs, detailed

descriptions and literature overviews are given for every explaining factor.

18

Table 5: Explaining factors for SOC and AGB that are investigated

Explaining factor Measuring method Detailed description (section

number)

Diversity index Based on biomass data collection 2.3.1

Soil organic carbon Walkley & Black (1934) 2.3.2

Permeability Saturated hydraulic conductivity

meter

2.3.3

Slope Inclinometer 2.3.4

Aspect Compass 2.3.5

Altitude GPS 2.3.6

Temperature Online database 2.3.7

Precipitation Online database 2.3.8

Bulk density Oven drying 2.3.9

Particle density Pycnometer 2.3.10

Stoniness Estimation 2.3.11

Tree, shrub and herb cover Estimation 2.3.12

Fodder suitability Estimation 2.3.13

Grazing pressure Estimation 2.3.14

Tree cutting Estimation 2.3.15

Fire incidence Estimation 2.3.16

Erosion status Estimation 2.3.17

Management Field measurement 2.3.18

Distance to streets and villages PostGIS 2.3.19

2.3.1. Diversity index

The diversity of a plot got calculated based on collected biomass data and is not a metric that is determined

directly on the field. To express the diversity, we use an “index adjusted for two plots” (Van Eetvelde, personal

conversation, 17/10/2018). This index was determined by calculating the Shannon diversity index of a plot

and by dividing it by the total number of species over all the exclosures. This gets done separately for the 20

by 20 and the 5 by 5 plot. These two outcomes are added up to obtain one final result for each plot. This

result will be in the range of minimal 0 to maximal 2. Literature showed that the biodiversity has a positive

effect on the SOC (Saha et al., 2009; Steinbeiss et al., 2008).

19

2.3.2. Soil organic carbon

The soil organic carbon of each plot got determined based on the disturbed samples. This work was done by

the University of Mekelle. Their researchers are using the method proposed by Walkley & Black (1934),

details of this method are detailed explained in paragraph 2.2.

2.3.3. Permeability

The permeability of the soil was determined in the laboratory of Mekelle University. They use the undisturbed

samples and the hydraulic conductivity meter to determine this variable. The working of the hydraulic

conductivity meter is further explained in paragraph 2.4.

2.3.4. Slope

The slope of the terrain was determined directly on the field with the help of an inclinometer. An accuracy

measurement was acquired by putting a person at the bottom of the plot and another at the top of the plot.

Then, the slope was measured from eye to eye. Errors occurring due to the difference in height between the

two persons proved to be around 1 degree at maximum. Literature of Ritchie et al., (2007) found a negative

correlation between SOC and the slope of the terrain.

2.3.5. Aspect

The aspect was measured directly on the field with an analogue compass. After the primary and analog

compass that was brought from Belgium showed signs of malfunctioning due to the interference with GSM

batteries. The digital, 3-axis electronical compass build-in into the GPS was used. For every plot that was

visited by Jonathan De Deyn, the aspect was measured in degrees. The plots that were not visited by

Jonathan, only have an indication of the aspect expressed as wind direction. In the literature, the influence

of aspect is showing contrary results. Lal, (2005) found higher SOC concentration on north-eastern slopes

while Yimer et al., (2006) executed a study in the Bale mountains of Ethiopia and found higher SOC

concentrations in the southern aspect.

2.3.6. Altitude

The altitude was automatically determined by the GPS when the coordinates of the plots were saved. To

make sure that the GPS was having the best accuracy as possible, it was never turned off during the day.

By doing this, we had a constant connection with the satellites and the lowest possible vertical error of 3

meters. Two GPS’s were always with us on the field, this made sure that we could manually check and adjust

20

the produced values. Research conducted in other study areas has found a positive correlation between

SOC and altitude (Dai & Huang, 2006; Wang et al., 2002).

2.3.7. Temperature

The average temperature (°C) of each plot was inserted into the database with the help of the GIS-software.

The data were derived from the WorldClim – a global climate database – with a spatial resolution of 30

seconds (Fick & Hijmans, 2017). We used the data of version 2.0. The data of WorldClim is produced by

interpolating weather station data using thin-plate splines, including databases with long-term averages and

data derived from satellite sensors (Fick & Hijmans, 2017). Previous conducted studies have found a

negative correlation between SOC and temperature (FAO, 2004; Jobbágy & Jackson, 2000; Wang et al.,

2002; Wang et al., 2004).

2.3.8. Precipitation

The average precipitation (mm/year) of each plot was inserted into the database with the help of the GIS-

software. The data was derived from the WorldClim – a global climate database – with a spatial resolution of

30 seconds (Fick & Hijmans, 2017). We used the data of version 2.0. The method used by WorldClim is the

same as described in paragraph 2.3.7. Numerous researches have indicated a positive correlation between

precipitation and SOC (Alvarez, 2005; Dai & Huang, 2006; Jobbágy & Jackson, 2000; Mekuria et al., 2011;

Wang et al., 2004).

2.3.9. Bulk density

The bulk density was calculated with the aid of the undisturbed samples before these were used to determine

the permeability. The method used to determine the bulk density is the method proposed by McKenzie et al.

(2004). Here, we used a standard metal core with a well-known volume and mass which is filled with an

undisturbed soil sample. First, this core sample was oven dried for 2 hours on 105°C to remove all the

moisture. The dry bulk density is then calculated as: BD = (w2 – w1)/V

Here, w2 is the weight of the core measured after oven drying; w1 is the weight of the metal core itself and

V is the volume of the core. The analyses were performed in the laboratory of Mekelle University. Bulk density

is expressed in g/cm³.

21

2.3.10. Particle density and texture class

The particle density and texture class were calculated based on the disturbed soil samples. Particle density

is the weight of an individual soil particle per unit of volume. The method used to determine the particle

density is the pycnometer method that uses distilled water as proposed by Carter & Gregorich, (2006). The

analyses were performed in the laboratory of Mekelle University. The used unit for particle density is g/cm³.

Soil texture class was determined by using sieve analysis, also known as the mechanical method, and

executed as proposed by Carter & Gregorich, (2006). Literature on soil texture and soil aggregation has

contradictory outcomes. Multiple studies (Jandl et al., 2007; Jobbágy & Jackson, 2000; Lal, 2005; Mekuria

et al., 2011; Wang et al., 2002) found that coarser soils have lower total SOC concentration while the study

of Alvarez, (2005) indicates that SOC concentrations decreases as the soil texture becomes more fine. The

study of Mekuria et al., (2007) also found that SOC is positively correlated to the available phosphorus (P)

2.3.11. Stoniness

The stoniness of the soil was estimated directly on the field. This concept indicates the percentage cover of

bigger and smaller stones on the surface. The part of the surface that is not covered by stones is covered by

bare soil. By making an estimation of the surface stoniness, the assumption is made that this percentage is

representative for the underlying, invisible layers. This stoniness can be interpreted as some kind of

roughness parameter of the ground. The final estimated value is always taken as the average estimated

value from at least two (most of the time even three or four) independent researchers. It is important to have

multiple researchers making an estimation because one is not able to make a very precise estimation by

himself. Stoniness is expressed in percentage. The estimation method is based on a personal discussion

with professor J. Nyssen during the beginning of the fieldwork (2nd week of July 2018).

2.3.12. Tree, shrub and herb cover

The tree, shrub and herb cover is expressed in percentage and was estimated directly on the field. Here

again, the final value taken for each variable is taken as the average value determined by at least two

independent researchers. The combination of the three values allows us to obtain an interpretation of the

biomass density in the plot.

2.3.13. Fodder suitability

Fodder suitability is an estimation of the suitability of the herb layer as fodder. This value is determined

directly in the field by at least 2 qualified researchers and is expressed as ordinal data. Table 6 shows the

used field codes and their description.

22

Table 6: Estimation of the suitability of the herb layer as fodder

Source: Ethiotrees

Code Class Description

1 High Excellent as fodder

2 Medium Can be used as fodder

3 Low Most herbs are not appropriate to use as fodder

2.3.14. Grazing pressure

Grazing pressure is expressed as ordinal data. This value gives an idea of the amount of grazing in the plot.

The value is chosen on the field by at least 2 qualified researchers and is based on signs of present dung,

eaten leaves and in extreme cases cattle itself. Table 7 shows the used field codes and their description.

The method is based on the method used by Salvatori et al., (2003) who used percentages of lost grass

cover to indicate the grazing pressure. We extended this method to losses of leaves in general and added

the presence of dung and hoof marks as indicators. Literature has shown that grazing has a negative impact

on SOC concentrations (Parton, Schimel, Cole, & Ojima, 1987; Salvatori et al., 2003).

Table 7: Grazing pressure level estimation

Source: Ethiotrees


1 High Cattle seen or fresh cow-dung seen and hoof-marks visible. Soil compacted due

to trampling, plants also trampled. Grass eaten

2 Medium Cow-dung seen at one or two places, hoof marks visible. Soil may not be

compacted, grass also visible.

3 Low Hoof marks and cow-dung may not be visible. Soil not compacted but some signs

of grazing visible in the area.

4 Absent No sign of grazing

2.3.15. Tree cutting

Tree cutting is expressed as ordinal data. This value gives an idea of the amount of illegal tree cutting by

farmers. In the field, this value is chosen by at least 2 qualified researchers and is based on damage on

stems and cut stamps. Table 8 shows the used field codes and their description. The study of Lal, (2005)

found that harvesting op biomass can cause a decline of SOC up to 50%.

23

Table 8: Cutting lever estimation

Source: Ethiotrees


1 High Most of the trees are badly cut and chopped, cut stamps are visible

2 Medium Trees only partially damaged with the main stem often intact

3 Low Signs same as above but on a sporadic scale or less intense

4 Absent No tree cutting signs observed

2.3.16. Fire incidence

Fire incidence gives a description of recently occurred fires and intensities. The value is expressed as ordinal

data and is chosen by at least 2 qualified researchers. The used codes are explained in Table 9. The method

is based on the method used by Salvatori et al., (2003) who also used signs of burnt vegetation and presence

of charcoal to identify classes. Salvatori et al., (2003) found that fire incidences limit the natural regeneration

capacity of woodlands.

Table 9: Fire incidence observation and classification

Source: Ethiotrees


1 High Stems are blackened, bark is burnt, crown is burnt and some trees died because

of fire. Understory vegetation burnt and soil is charred.

2 Medium Stems are blackened, bark is burnt, crown not burnt and trees not dead,

Undergrowth burnt, soil is charred, bark may be slightly blackened or burnt.

3 Low Undergrowth burnt, burnt twigs can be found. Soil may be charred.

4 Absent Fire totally absent

2.3.17. Erosion status

Erosion status puts an ordinal value into the observed amount of erosion is a plot, the value is chosen by at

least 2 qualified researchers. Table 10: gives the used codes and their description.

24

Table 10: Erosion level determination

Source: Ethiotrees


1 No erosion No signs of erosion

2 Slight sheet erosion Thin layer of topsoil moved

3 Moderate Sheet erosion and rills

4 Strong Small gullies, exposed plant roots

5 Severe Large gullies and landslides

2.3.18. Management

The last explaining factors that were determined are all related to the management of the exclosure. First,

we looked at the nature of the exclosure and its age. These data were collected by performing a short

interview with a guard or a nearby living adult, most of the time, this were the people who came to us when

working in the exclosure. By collecting this data in the form of an interview means that the age of the exclosure

is only a rough estimation and not precise value. This method had to be used since there is no governmental

documentation about the exact establishment date of the exclosure. Interviews were often limited to two

persons for each exclosure. Multiple studies on biomass regenerating projects already found a positive

correlation between carbon sequestration and the age (Mekuria & Veldkamp, 2012; Mekuria et al., 2011).

In each 20*20 plot, we also measured the management in the form of the total length of existing stone bunds

(in meter), the number of percolation ponds above the plot, the number of trenches in the plot, the number

of half-moons in the plot and at last, the number of guards responsible for protecting the exclosure. Literature

has proven that management that maintains a continuous canopy cover is positively correlated with C storage

(Lal, 2005).

2.3.19. Distance to streets and villages

The distance between a plot in an exclosures and the nearest by village is calculated using PostGIS, the

same is true for the distance to the street. The data on localization of villages and streets is obtained by the

Ethiotrees project and is the same data as used for the Geo-trekking map in the book of Nyssen et al., (2019).

Based on a small assessment of the data and a quality check by some small-scale sampling by myself, I

considered the data as reliable. A bigger dataset on streets and walking paths is available on

OpenStreetMap. These routes are however very selective and do not represent the major walking paths

taken by the locals. This makes that this dataset is not applicable for our research purposes.

Examined literature has shown that the distance to a village from the plot and the AGB measurements are

positively correlated (Baskaran et al., 2012; Christensen & Heilmann-Clausen., 2009) which already indicates

that this human aspect could be an important explaining variable.

25

2.4. Water infiltration

2.4.1. Water mass balance for exclosures

Numerous studies have indicated that the establishment of exclosures can be linked with better water

infiltration (Descheemaeker et al., 2006; Girmay et al., 2009; Nyssen et al., 2010), this statement is partially

supported by the fact that SOC improves the soil moisture retention (Rawls et al., 2003). The first question

that needs to be answered is where the infiltration water is used for. In other words, what is the water balance

in an exclosure? The answer is provided by Descheemaeker et al. (2009). The water mass balance for

exclosures in North Ethiopia (Figure 5) shows a rapid increase in infiltration and transpiration after the

establishment of the exclosure. The evaporation will first increase and later decrease due to shading and

mulch development. The deep percolation of water will rise from 3% of the total precipitation on degraded

land to 30% in old exclosures with runon. This runon is the additional input of water in exclosures due to

overland flow on higher ground (Descheemaeker et al., 2009). The exclosures are part of the source-sink

system for water in which all the runon water infiltrates in the first 50 meter of an exclosure (Descheemaeker

et al., 2009).

Figure 5: Vertical soil water flows (expressed as percentage of rainfall) for five steps in the evolution of an exclosure (RA, degraded

grazing land; XY, young exclosure; XM, middle-aged exclosure; XO1 and XO2, old exclosures with normal and dense shrub cover,

respectively (a) without runon (b) with runon

Source: Descheemaeker et al., (2009)

26

2.4.2. Measuring groundwater recharge techniques

To measure groundwater recharge to make predictions about groundwater levels in general, numerous

techniques are available (Table 11) which differentiate them from the others in scale in space and time but

also in reliability (Scanlon et al., 2002).

Table 11: Techniques to estimate groundwater recharge

A distinction has to be made between techniques that are based on surface water and water in unsaturated

zones that estimates the potential of groundwater recharge and techniques that use groundwater data to

calculate the actual recharge (Scanlon et al., 2002). Many of the techniques are based on the water-budget

method, this method estimates the recharge of groundwater by calculating all the other variables in the water

balance equation (Scanlon et al., 2002). The subsurface water mass balance in an open system can be

written down as following, equation (3) (Nyssen, course hydrology 2017, UGent):

ΔSg= I + Gin - Gout – Qg –Eg –Tg (3)

Here, ΔSg stands for the difference in groundwater storage, I is the infiltration, Gin and Gout are respectively

the groundwater inflow and groundwater outflow, Qg is the loss due to a spring, Eg is the evaporation of the

ground and Tg the transpiration of the ground. This study examines the influence of SOC on the groundwater

recharge and is thus linked with the infiltration parameter of equation (3). When the mathematical assumption

is made that all parameters except infiltration remain the same, a better infiltration will provide an increment

of Sg and thus a higher groundwater table. To link SOC to the infiltration capacity of a soil and thus

groundwater recharge, measuring techniques for the infiltration rate of the soil are necessary. One technique

to apply on the field is the use of a single (or double) ring infiltrometer which consists of a metal cylinder to

insert into the ground and a permeameter to calculate the field saturated infiltration rate (Lanckriet et al.,

Method Notes Source

Water-budget method Accuracy of recharge is limited to

the accuracy of other

components

(Scanlon et al., 2002)

Techniques Based on surface-water

Channel water budget Specific for stream systems (Scanlon et al., 2002)

Seepage meters Inexpensive and easy to apply

In the stream of lake

(Scanlon et al., 2002)

Guelph permeameter / (Lanckriet et al., 2012)

27

2012). It is also possible to work the other way around and to take undisturbed core samples from the field

and analyse them with a saturated hydraulic conductivity meter.

2.4.3. Soil moisture retention

The soil water can be divided into three main categories: the groundwater, the capillary water and the

pendular water (Verheye and Ameryckx, 2007). The pendular water is the water has been attached to soil

particles or is left behind in pores while seeping through the soil (Verheye and Ameryckx, 2007). This

pendular water is in the literature often named as the soil water retention, this is thus the soil water that is

retained in the soil despite the gravity pulling it downwards. The soil water is important due to its wide range

of applications: germination of seeds, transport of nutrients, evapotranspiration and even for the living fauna

in the soil (Verheye and Ameryckx, 2007). In semiarid areas, soils contain few organic matter and have a low

water-holding capacity which will result in moisture stress for the growing vegetation (Descheemaeker et al.,

2006; Taddese, 2001). Descheemaeker et al. (2006) note that the implementation of exclosures on steep

slopes has a positive impact on the soil and water conservation. The average daily runoff coefficients on

steep slopes reduces from 50% on degraded land to 30% in young exclosures (5 years) to 2% in old

exclosures. This implies that almost all the water from a precipitation event will infiltrate in the soil and be

available for the vegetation or as groundwater supply or evapotranspiration (Descheemaeker et al., 2006).

This statement is supported by higher soil moisture content in exclosures compared to the degraded land

over different soil depths. The soil moisture content in degraded land will throughout the year always fluctuate

around the wilting point while for exclosures, this is only in the dry season (Descheemaeker et al., 2006). The

higher infiltration rate has multiple explaining factors (Table 12) most of them are due to the reduced water

velocity and energy and thus the expanded time to infiltrate.

Table 12: Benefits of vegetation cover in comparison to the bare ground for groundwater infiltration and recharge.

Reason Explanation Source

Canopy cover -Intercepts raindrops and reduces the

kinetic energy

-Infiltration pathway

Morgan et al., 1986

Standing biomass and litterfall Surface roughness reduces water

velocity and gives it more time to infiltrate

Morgan et al., 1986

SOC Improves soil moisture retention (Rawls et al., 2003)

Vegetation roots Helps to loosen rock and soils (Yeh et al., 2009)

Shadow of vegetation Prevents direct evaporation from the soil (Yeh et al., 2009)

28

2.4.4. Determination of saturated hydraulic conductivity

The saturated infiltration rates of the topsoil were obtained by collecting two undisturbed soil samples in each

plot for an analysis in the laboratory using the hydraulic conductivity meter (Picture 8). These samples were

collected on the same location where the SOC samples were taken and where the AGB was measured such

that all the data could be compared. The infiltration rate was then calculated using the following formula: Ksat

= (V*L)/(A*h*t) in which Ksat is the saturated hydraulic conductivity of the soil expressed in cm/s; V stands for

tot total volume of the core (in cm3); L stands for the length of the core where the core is filled by the soil (in

cm); A stands for the cross-sectional area of the core samples expressed in cm² (Picture 9), h is the difference

in height of the internal and external water (in cm) (Picture 10), t stands for the time that is measured and is

expressed in seconds. All the practical specifications of measuring the saturated hydraulic conductivity are

further supported by visual material in Table 13.

Table 13: Visual clarification of the use of the saturated hydraulic conductivity meter

Picture 8: Saturated

hydraulic conductivity meter

Source: own picture, 22/08/2018

Picture 9: Filled,

undisturbed core


Picture 10: Height difference between internal

(1) and external (2) water


As stated above, the undisturbed samples were taken in duplicate, so two samples for each plot. Each

sample was given the same code as the code of the plot, with an additional number (1 or 2) that indicates

the first or second sample for that plot. The samples were taken in the centrum of the plot on representative

places. Keeping in mind that these undisturbed samples were collected to investigate the bulk density and

the permeability, we carefully took them on places where none of the researchers had stepped on. Picture

11 shows the different phases applied when collecting these undisturbed samples. In the first step, the upper

layer of the soil gets removed of stones and litter by hand. In the second step, the core gets inserted into the

ground by gently hitting it with a hammer. No hard hits were allowed because these causes vibrations that in

turn break the ground. This has as a consequence that the sample is no longer undisturbed and thus reliable.

This process gets repeated until the core is completely in the ground. Once the core is completely filled, no

extra hits are permitted because this would make the sample more compacted in comparison to the

(1)

(2)

29

environment (step 3). The fourth step consists of digging out the sample with care and to top off the soil at

the bottom (step 5). When the core was not completely filled or when a rock was inside, and this rock

disallowed us to seal the core properly, the whole process got redone. At last, step 6, a high-quality core

sample got sealed, labelled and boxed.

To be able to make non-debatable conclusions on the effect of exclosure on water infiltration rates. We will

work with so called ‘twin catchments’, a concept that has previously been pointed out by Mekuria et al.,

(2011). With this term, we mean that we realize the plots in the grazing land on areas that have the same

physical characteristics as the exclosure. This makes that potential founded differences in saturated hydraulic

conductivity can only be explained by the effect of the exclosure and thus not by differences in lithology or

soil characteristics. If there is no grazing land that can act as a twin catchment for a given exclosure,

undisturbed samples are only collected in the exclosure.

Picture 11: Different steps of collecting an undisturbed sample


3. STATISTICAL METHODS

Our dataset consists of four independent variables namely AGB, SOC, permeability and bulk density. We

use the Shapiro-Wilk’s test to compare the sample distribution of these variables to a normal distribution. The

Shapiro-Wilk’s test rejects the null-hypotheses (corresponding p-values: 1.08*10^-5; 0.0003; 1.31*10^-5;

2.1*10^-5) for each variable which indicates that the data is not normal distributed. Non-parametric test needs

to be used in all the statistical analyses. The dataset consist of unpaired continues and categorical data.

A correlation analyses between two continues variables is done by using the Spearman correlation coefficient

as this coefficient does not assume normal distribution of the data (Hauke & Kossowski, 2011). The statistical

test used to compare a non-normal distributed independent variable with a categorical variable that consist

of more than two groups is the Kruskal-Wallis test (Gooch, 2011). Post hoc analyses on the Kruskal-Wallis

test to determine a significant difference between groups is done by the Dunn test using the Benjamini-

30

Hochberg method. The Dunn test is used because this test is appropriate for groups with an unequal number

of observations (Zar, 2010). In order to investigate if there is a significant difference in permeability and bulk

density in grazing land and exclosure, we use the Mann-Whitney U test as this test does not assume a normal

distribution of the data (Nachar, 2016).

As soon as we start analyzing on the level of exclosures, the data is normal distributed. We only work on the

level of exclosure to compare twin catchments (exclosure vs. grazing land). This implements that we need

to use an unpaired t-test instead of the Mann-Whitney U test. The Welch’s t-test for unequal variances is

used (default t-test in ‘R’).

All the statistics and graphs are calculated in the statistical software package ‘R’.

31

4. RESULTS

4.1. Carbon sequestration

Data on above ground biomass has been collected in a total number of 195 plots. From all these plots, it was

not possible for 10 plots to link the collected data with a GPS-point. This means that in the further analyses,

we will only take the 185 plots in consideration of which the exact location is known. These plots are spread

out over 16 exclosures. A total number of 16.643 tree/shrub/herb stems were measured in these 185 plots.

Figure 6 shows the distribution of AGB in each exclosure in the form of boxplots, values are variating from

less than 100 kg of biomass in a plot in Afedena to almost 1400 kg of biomass in a plot in Gojam Sfra. This

indicates a great variance in AGB between Exclosure. Big variations are not only found between but also

within exclosures, when taking Togogua as an example: The AGB values in plots range from a first quartile

value of +/- 300 kg to a third quartile value of around 800 kg, the maximal value even goes over 1200 kg.

Deeper spatial analyses in GIS-software will try to explain these inter- and intra- exclosure variability.

Figure 6: Boxplot of the above ground biomass (AGB) distribution in plots for each exclosure

Table 14 gives an overview of the concentration of carbon that is stored in every exclosure. A separation has

been made between the total concentration of C, the concentration on SOC and the concentration of carbon

stored in biomass (= AGB+BGB). The concentration of SOC is still in research for Adi Lihitsi and Gidmi

Gestate, this causes the absence of their SOC concentration and their total C concentration in the table. The

exclosure of Maybate has been split into two separate exclosures, this because the exclosure is an

aggregation of two different exclosures belonging to different villages. These two exclosures have a different

anility which is of importance in further analyses. The table indicates that the carbon sequestration in the soil

is on average 10 times higher than the carbon sequestration in the biomass. This means that for carbon

sequestration programs, the SOC is of higher importance than the carbon sequestered in biomass. However,

it is important to note that the Carbon “from the atmosphere” is not directly stored as SOC but that it takes

three stages to get there. First, carbon dioxide is removed from the atmosphere by photosynthesis. Second,

the carbon dioxide gets transformed into biomass and third, a transfer of carbon from the plant biomass into

32

the soil (FAO, 2017). This means that it is actually more important for projects to have good management

and understanding of the living biomass to promote further carbon sequestration in the soil. This statement

gets confirmed by our results where we conducted a Spearman correlation test and found a significant

positive correlation between the above ground biomass of a plot and the corresponding SOC of that plot

(Figure 7) with a p-value of 0.008777.

Table 14: Overview of the concentration of total carbon (Ton C/ha), biomass (Ton C/ha) and SOC (Ton C/ha) for each

exclosure.

Exclosure Total Carbon (Ton C/ha) Biomass (Ton C/ha) SOC (Ton C/ha)

Maybate (combined) 73,450 5,646 67,804

Maybate1 74,363 5,756 68,607

Maybate2 72,702 5,567 67,135

Lafa 74,499 13,871 60,628

Adilal (Ta Akuro) 52,371 11,246 41,125

Togogua (Daero Hidag) 87,459 8,332 79,127

Tokul 72,584 8,549 64,035

Sesemat 66,749 9,482 57,267

Adi Meles 95,216 7,127 88,089

Cheleqo (Habako) 94,351 10,978 83,375

Gemgemma 53,086 6,603 46,482

Meam Atali 71,740 5,893 65,847

Adi Lihitsi 11,024

Gidmi Gestate 8,986

May Genet 68,657 5,731 62,926

Mey Hibo 96,076 8,821 87,256

Afdena 66,218 5,914 60,304

Zban Dake 37,563 8,726 28,837

Katna Ruwa 11,348

Gojam Sfra 13,029

33

Figure 7: Correlation between above ground biomass and soil organic carbon

4.1.1. Influence of Age and management

To explain the difference in AGB, we first investigated a correlation with the most logical independent

variable, namely the time that has passed since the exclosure was established. Applying the Spearman

correlation statistic on the dependent variable AGB and independent variable age, statistics are showing a

significant positive correlation with a p-value of 6.2*10^-4 (Figure 8).

Figure 8: Correlation between AGB and age

34

Looking at Figure 8 gives the indication that age seems to follow a more categorical distribution. This can be

explained by the numerous plots in one exclosure who are all having the same age. To deny this effect, first,

we change the variable age from a continuing variable to a categorical variable. Following categories are

made; the age of 1 to 10: category 1; age 11 to 20: category 2; age 21 to 30: category 3. These categories

are going on until every plot can be accommodated in a single category. Running a Kruskal-Wallis test on

this manipulated data, we again found the p-value of 0.00017 and thus again a significant positive correlation

(Figure 9). The Dunn test only found an adjusted p-value lower than 0.05 between category 1 and 3.

Figure 9: Distribution of the AGB of a plot in the different categorized age classes

The second manipulation of the data is done by working on the level of Exclosures. The positive side of this

manipulation is that we can now see the age as a continuous variable that does not depend anymore on the

number of plots taken in a certain exclosure. The downside, however, is that this manipulation sharply

reduces the number of observations to 19. This equals the number of exclosures +1 (Maybate is divided in

two). From these 19 exclosures, we only have the age available for 11 exclosures. This makes it more difficult

to find statistical correlations in the dataset. The analyses are also more sensitive to extreme values. Because

this analysis is done on the level of Exclosures, it was no longer possible to work with AGB. As a result of

this, AGB is transformed into biomass concentrations expressed in Ton C/Ha. Figure 10 visualises this

correlation, a Spearman correlation test does not give significant results. We can, however, indicate that the

correlation line is heavily influenced by the extreme age of 55 years (Maybate exclosure). Without this value,

the observations appear to have a more linear or even exponential correlation. This non-statistical correlation

on the level of exclosures with age seems to be counter-intuitive and strange. The first explanation is already

35

given above with the effect of fewer observations and extreme values on statistics. A second explanation is

that we make the assumption that the initial amount of biomass represent in an area before it is converted

into an exclosure is the same.

Figure 10: Correlation between biomass concentration and the age of an Exclosure

We will now consider the oldest part of the exclosure of Maybate as an outlier. This consideration is statistical

justified as can be seen in Figure 11. This means that the same correlation analysis is executed without the

observation ‘Maybate1’ with his age of 55.

Figure 11: Data distribution of the variable ‘Age’ on the level of Exclosures

36

Results of this Spearman correlation test now indicates a significant correlation between biomass

concentrations and the age of an exclosure with a p-value of 0.01615. The linear correlation is represented

in Figure 12 where the regression line indicates a linear increase in biomass of 0.226 ton per year with a

starting carbon concentration of 3.52 ton C/ha when the exclosure is established. Removing the BGB from

this value, we obtain an initial carbon storage of 2.83 ton C/ha. Comparing this value with the above ground

carbon measurements on grazing land as executed by the study of Mekuria et al., (2011) who are having

concentrations ranging from 1.1 to 1.9 ton C/ha. We can only conclude that this linear regression makes an

overestimation of the initial carbon content. This could be due to inaccuracies in the exact age, due to already

beneficial site conditions when the exclosure was established, due to higher management levels in the

younger exclosures or just the result of poor regression analyses due to the little observation numbers.

Figure 12: Correlation between biomass concentrations and Age of an exclosure, when considering Maybate as an outlier

Analysing the influence of exclosure age on the amount of carbon stored in biomass has now been

completed. In the next phase, we will do the same analysis to discern the effect of age on the SOC. We will

first work on plot-level and secondly on the level of exclosures. On the level of plots, we did not found a

significant positive correlation (p-value: 0.2949). The correlation is shown in Figure 13. We observe that that

dataset contains one or more exclosures with an age of 8 years of which the SOC concentrations are already

notably higher. These exclosures are Meam Atali and May Genet.

37

Figure 13: Correlation between SOC and the age of the exclosure analysed on the level of plots

Working with the categorized age classes (Figure 14), the structure of the data becomes more clear. Ignoring

the first category, the SOC seems to have a linear increase over time. The Kruskal-Wallis test confirms this

statement by producing a p-value of 0.0095. The Dunn test indicates a significant difference between

category 2 – 3 and 2 – 6. There is no significant correlation between category 3 and 6. However, it not

possible to just ignore category 1 and the logical question: “Why is age category 1 having SOC concentrations

that are similar to category 3? How does it come that the difference between category 1 and 2 is the most

significant difference with an adjusted p-value of 0.018?” Looking at the properties of the exclosures situated

in this category a very interesting characteristic comes above water, namely that all these young exclosures

are subject to intensive management whereas the more elderly exclosures, except Tokul and parts of

Gemgemma, are not having any management at all. Could this influence of management be the relatively

simple but very interesting answer to our questions? The most simple and clean way to test this is to find

intra-exclosure variability on SOC related to differences in management. Here, we know that all other

variables are closely related to each other. In the original database, management is divided into stone bunds,

percolation ponds and trenches. We simply reduced these three variables into binary variables for areas with

management and areas without management. Two exclosures can be tested for the intra-exclosure

variability, namely Gemgemma and Afedena.

38

Figure 14: Data distribution of the SOC in the different categorized age classes

The results of Gemgemma and Afedena are represented in Figure 15. In neither of the exclosures, we found

statistical evidence for the impact of management on SOC. Both exclosure have however higher median

SOC concentrations in their parts with management. An explanation for the non-correlation can be due to

the fact that there is simply no correlation between the two variables or due to the very low number of

observations. To create more certainty in this analysis, we will now work on inter-exclosure variability. We

select all the exclosures that have an age of 20 to 26 years as we have exclosures with and without

management in this timestep. In this selection, we have the exclosures of Tokul, Maybate and part of

Gemgemma with management and the exclosures of Sesemat, Adilal and the other part of Gemgemma as

exclosures without management. Age should not bias the results as the exclosures with management are

the younger exclosures. Results are visualized in Figure 16. We again have the tendency to conclude that

management indeed influences the SOC, but this statement is not statistically supported.

Figure 15: Data distribution on SOC in Afedena (left) and Gemgemma (right) for differences in management

39

Figure 16: Data distribution of SOC in exclosures with and without management with an age between 20 and 26 years

Statistical analysis between the AGB of a plot and the management indicates a significant difference between

places with and without management, here the places without management are having more AGB (Figure

17). To explain this paradoxical result, a relation is searched between management and the age of the

exclosure. It is however not possible to run an analysis on the binary variable ‘management’ and the

categorized variable ‘category age’. To overcome this problem, an analyse is done between the categorized

variable ‘category age’ and the continue variable ‘stone bunds’. The change of the variable ‘management’

into the variable ‘stone bunds’ can be justified as the dominant form of management in an exclosure is in the

form of stone bunds. This form of management is also always present when other management forms are

implemented. Results of the analyses are represented in Figure 18. Statistics clearly indicate the dominance

of stone bunds (and thus management) in the younger exclosures. This explains the paradoxical correlation

between AGB and management. The management is only implemented in the more recent established

exclosures where the biomass regeneration process is still very active. The older exclosures where the

biomass regenerations process is in a further stadium, no extra stimulation for the regeneration in the form

of management is implemented.

40

Figure 17: Data distribution of AGB separated for areas with and without management. Significant different with a p-value of

0.0004.

Figure 18: Data distribution of the total length of stone bunds in a 20 * 20 plot for the different age categories. Significant

difference between: 1-3; 1-6, 2-3, 2-6

4.1.2. Influence of climatic variations

Influences of local climate fluctuations on the AGB is analysed on the exclosure level. We have chosen to

analyse on this level because the climate data has a spatial resolution of one kilometre while we are having

biomass measurements on a resolution of 150 meters. To determine the mean annual precipitation and

temperature of exclosures. We used the zonal statistics function in ArcMap. The Pearson correlation test did

not show significant correlations between biomass concentrations and temperature or precipitation. This

41

means that according to our results, local climatic fluctuations do not affect the biomass regeneration process

in terms of speed.

4.1.3. Influence of soil texture and soil class

The next variable that is evaluated for its potential influence on AGB and SOC is the influence of the soil.

Here, we examine the effect of the soil composition and the associated soil class. Analyses are done on the

level of plots. A Spearman correlation coefficient only indicated a significant influence of silt on the AGB

(Figure 19). For the clay fraction, we have the tendency to find a positive correlation between the two

variables while for the sand fraction, no clear line was observed. These last two statements are statistically

not supported. The correlation between the soil class and AGB is represented in Figure 20. The statistics

indicated a significant difference between loam and say clay loam, say clay loam and silt loam. Comparing

these tree soil classes on a soil classification system (Figure 21) we observe that the soil class with the higher

AGB, the say clay loam class, is the class with the highest amount of clay. Compared to the other two classes,

the say clay loam class has a proportion of silt varying between 40 and 70 percent, which is more or less the

same as the silt fraction in loam and silt loam classes. The differences between the soil classes are situated

in the sand and clay fraction. The say clay loam class has a maximal fraction of sand of 20 percent and a

clay fraction of at least 30 percent. The other two classes are having sand fractions up to 50 percent while

the clay fractions do not exceed 30 percent. This all indicates that the higher biomass measurement can be

partially explained by the higher clay and lower sand concentrations in the subsoil. One explanation for these

observations is that clay soils are having lower water infiltration speeds and are thus holding the available

water for a longer time (Rawls et al., 1982). This means that the regenerating biomass has water available

over a longer time period during the day and will experience less stress due to droughts. In a dry region like

the Dogua Tembien region, this could have a major influence. These results are in line with the results

founded by Mekuria et al., (2011) who also founded a positive correlation with the precipitation to indicate

the importance of water availability.

42

Figure 19: Correlation between AGB and %silt in the soil. Negative correlation with a p-value of 0.0019.

Figure 20: Data distribution of the AGB over the different soil classes. Statistical significant differences are found between the

blue and red boxes combination.

43

Figure 21: Soil classification system, the red and blue colours indicate the soil classes where a statistically significant difference

in AGB was found.

Source: www.nrcs.usda.gov; 22/04/2019

We will now run the same statistical models but instead of using AGB, we now use SOC as an independent

variable. A Spearman correlation statistic indicates again a significant negative correlation with the amount

of silt in the soil (Figure 22). In contrast to the AGB, statistics did not only seem to have a positive correlation

with the clay fraction. This time, this correlation is also statistical significant (Figure 23). This observation only

strengthens our previous assumptions that the proportion of clay in the soil has an important influence on the

carbon stock. Analysing the impact of the soil class on the SOC (Figure 24). We found a significant difference

between following classes: clay – loam, clay – say loam, clay – silt loam, clay loam – silt loam, say clay loam

– silt loam, clay – silty clay loam. These categories are again indicated in blue and red colours in Figure 24.

The effect of the clay fraction in the soil is here clearly visible. Soils that contain more clay have the tendency

to capture more SOC, this is in line, and maybe a direct effect, of the effect of the clay content on the AGB.

44

Figure 22: Correlation between SOC and %silt in the soil. Negative correlation with a p-value of 0.0093

Figure 23: Correlation between SOC and %clay in the soil. Positive correlation with a p-value of 0.00238

45

Figure 24: Data distribution of the SOC over the different soil classes. Statistical significant differences are found between the

blue and red boxes combinations. The darkest red colours are linked with the darkest blue colours, vice versa for the brightest colours

4.1.4. Influence of terrain characteristics

An evaluation of the influence of terrain characteristics on the AGB did not produce any significant

correlations. We tested for the slope of the field (Spearman correlation test), the aspect (Kruskal-Wallis test)

and stoniness (Spearman correlation test). For the SOC, the same results are found for slope and stoniness.

The impact of aspect on the SOC produced a significant correlation (Figure 25) Significant differences are

found between the following categories: E-N, NE-N, E-NW, NE-NW, N-S, NW-S. These categories are

indicated in Figure 25 with red and blue colours where the red-blue combination indicates a significant

difference. This indicates that SOC concentrations have the tendency to be higher in the eastern and

southern directions compared to the northern directions. I, however, believe this apparent correlation gives

a false image of the real world. For this, I will give two arguments. The first argument is by looking at the

shape of an exclosure. Exclosures are namely established on steep degraded soils that are often

concentrated on one ridge. This means that within one exclosure, all plots are having a more or less similar

orientation. This makes that the founded differences in SOC over the different aspects are actually just the

results of different orientations of the different exclosures. We actually found intra-exclosure variability in

SOC that can be explained by other factors. The second argument is that, if there is a real importance of

aspect on SOC, we would expect the differences to be in the more general wind directions like north-south

or east-west. We found a difference between north and northeast. These two wind directions are only having

a 45° variation. We would also expect that there is somewhat a linear decrease between two significant

different categories, this is not the case in our data as the NE category is having similar values as the S

category.

46

Figure 25: Data distribution of the SOC over the different wind directions. Statistical significant differences are found between

the blue and red boxes combinations. The darkest red colours are linked with the darkest blue colours, vice versa for the brightest

colours

4.1.5. Influence of human behaviour

At this phase, we have examined the influence of the soil, the management, the terrain characteristics, the

climate and the age of the exclosure on the AGB and SOC. The last unanswered question is the effect of

human behaviour on the success rates of carbon sequestration. This human behaviour is measured in the

form of illegal tree cutting and illegal grazing of cattle in the exclosures. The field methods for this have been

previously described in paragraph 2.3.14 and 2.3.15. In these analyses the data of Togogua has been

excluded from the dataset as this data was confirmed as unreliable by the external field researcher.

An analysis of AGB and grazing pressure produces significant results (Figure 26). Statistics are showing that

plots without grazing pressure (category four) are having significant more above ground biomass than plots

with grazing pressure (category two and three). The Kruskal-Wallis test calculated a p-value of 0.025.

A quick manipulation of the data of Togogua to what I believe is correct and reliable data produced even

lower p-values and thus more significant different differences between the categories.

To make a more in-depth analysis of these results and to find the underlying explanation for this correlation,

the data on AGB is visualised in the GIS – software. Here, we will try to find correlations between AGB and

the distances to villages and connection to villages.

47

Figure 26: Data distribution of AGB in the different grazing pressure classes. A significant difference is found between the

following categories: 2-4 and 3-4.

The distance between every plot and the nearest by village is calculated using PostGIS. A Spearman

correlation test on AGB and the distance to the nearest by village found a significant positive correlation with

a p-value of 0.03043 (Figure 27). This indicates that more biomass is found in places that are more isolated

from villages. This correlation clearly indicates the importance of human pressure on the biomass

regenerating process. As an explanation, we could state that the parts of an exclosure that are more easily

accessible are also more influenced by illegal activities. These illegal activities are going from tree cutting to

grazing by cattle. This is in line with the rational thinking of all species, namely that resources are collected

on the places that are having the lowest resistance to reach. This is why parts of the exclosures that are

more closely related to villages are having lower biomass values and parts that are further removed and that

are well isolated are having higher biomass values. This statement can be invigorated by the fact that

exclosures, despite it is illegal, are a very reliable source for grasses to graze and trees to cut. Two essential

resources that are scarce in the close proximity.

48

Figure 27: Correlation between AGB and distance to the nearest village.

Analysing the data on the level of the exclosure, we only find a significant correlation for Togogua (p-value:

0.00242) and Meam Atali (p-value: 0.00856) between AGB and the distance to the village. We will zoom in

into these two exclosures to declare the founded correlation. Figure 28 shows the AGB measurements in

Togogua and Meam Atali together with the nearby villages. The data is visualized on a satellite image base

map because no extended GIS dataset is available for this region. Satellite images provide us the best

possible visualisation of the reality. For the exclosure of Togogua, the lowest AGB measurements are found

in transect 1, this is the most northern transect near the road. The highest AGB measurements are found in

transect 4, these are the most south-eastern plots that are well isolated from nearby villages and streets. My

personal field experience tells me that reaching transect 1 or reaching transect 4 of Togogua are two different

levels of difficulty. To reach transect 4, you have to decline a steep hillside, pass multiple gullies and walk

over one hour. This is also a one-way-route, there are no other ways to reach this transect. In the north,

transect 4 is separated by a big gully while in the south, a cliff blocks the access. In the daily life of the farmer,

there are also no reasons to walk this way as there a no connections to other villages. A completely different

story is true for transect one. This transect is only separated from the road by a few hundred meters and is

very easily accessible for man and cattle. The road is also often used by cattle, especially when there is a

market in Togogua. On that day, households and their cattle are moving from Meam Atali to Togogua. It

might thus not come as a surprise that during this movement, some illegal grazing and tree cutting occurs.

These are also the days that the guards are going to the market and that the exclosure is left behind

unprotected. This state that the accessibility of the area has an impact on the AGB. In Meam Atali, the same

variation in biomass concentration is visible. Lower AGB values are founded on the south and south-eastern

part of the exclosure. The plots in this area are the plots that are nearest to the village of Meam Atali. Grazing

land in Meam Atali in situated north-eastern from the exclosure. This positioning makes that farmers and

their cattle have a good reason to pass the exclosure as the exclosure is laying somehow on the route

between the village and the grazing land. The exclosure of Meam Atali can also be defined as isolated as

there are no major streets with public transport within two kilometres of the exclosure and that Hashiwa, the

49

nearest village to the exclosure that is not Meam Atali, is also over a one kilometre walk from the exclosure

and is also more correlated with the exclosure of Lafa. These arguments make that we should not expect

severe grazing in the most northern parts of the exclosure.

It would be interesting to give this topic of the research a more qualitative approach to examine if the

awareness and happiness about exclosure are different for different villages and exclosure. This could be an

additional factor to explain the biomass regeneration in an exclosure and it could maybe even provide a clear

explanation between the founded correlation of AGB and distance to a village. As no such data has been

collected by myself. This link will no further be discussed in this report.

Figure 28: Visualisation of the AGB measurements in Togogua and Meam Atali

50

We’ previously proved the correlation between AGB and distance to villages, we will now run the same

analyses on the AGB and distance to major streets. Street data obtained from the Ethiotrees project was

used. This dataset includes the asphalt roads and the rural roads that are accessible by car. The numerous

hiking trails that are existing on the terrain are not included in this dataset as this data is until today still non-

existing. Mapping all the existing paths and digitalizing them would be a study on itself. The nearest distance

between the plots where AGB is measured and the streets are calculated in PostGIS.

A Spearman correlation test between the two variables produces a significant positive correlation (p-value of

4.49*10^-6). This indicates that more biomass occurs when a plot is further removed from a major street

(Figure 29). This again shows the importance of accessibility on AGB. This result only strengthens the results

founded on AGB and distance to the village.

Figure 29: Correlation between AGB and distance to the nearest street

If we do the same analyses on the level of exclosures, significant results are found for Gemgemma (p-value:

0.00446), Katna Ruwa (p-value: 0.00132), Meam Atali (p-value: 0.00379) and Togogua (p-value: 0.000393).

We will know zoom in into these exclosures to explain the correlations.

For the exclosure of Togogua and Meam Atali, the data is already visualised in Figure 28. The correlation of

AGB and distance to the street only confirms and strengthens our conclusions on the importance of

accessibility for inter-exclosure variability of AGB. For the exclosure of Gemgemma, AGB measurements are

visualised in Figure 30. The exclosure of Gemgemma in strongly north-south oriented with the street running

almost perfectly parallel with the exclosure. In this case, the founded correlation between AGB and the

distance to the street seems to be rather strange. This because of two reasons: the first reason is that the

street and the exclosure are separated by over 1.5 km while the village Merhib is only a few hundred meters

away from the exclosure. Over this distance of 1.5 km, it looks rather unlikely that this influences AGB plots

that are separated by only 150 meters. The second reason is formed based on my personal experience. The

51

exclosure of Gemgemma is situated nearby the intersection between the main asphalt road between Hagere

Selam and Mekelle where there is a lot of public transport and the more rural road between Togogua and

Hala(h) where public transport is very limited. This limitation in public transport between Hala and Togogua

makes that most of the farmers choose to walk from Hala instead of taking public transport. This makes that

we cannot consider the road as an equal distributor of possible impact on the exclosure. The most impact is

namely coming from the north where Hala can be seen as a local hub for farmers and cattle. It is sometimes

even more easy to get access to the exclosure when you approach it from its north-western side. Here, you

have more small villages with clear walking paths and easier accessible terrain. This in combination with the

public transport factor makes that it takes less time to reach the exclosure by doing a longer walk than by

following the streets and the public transport as long as possible. These arguments make that I believe that

the inter-exclosure variability of AGB in Gemgamma should not be explained by the distance to the nearest

streets. I believe that it has more to do with connections trails between villages or in some cases, even by

the fact that at the time of the establishment of the exclosure, some cropland has been converted into

exclosure. This makes that the initial stage of biomass was already different at the start of the exclosure. This

makes it only more difficult to find the real factors that affect the biomass regenerating process.

Figure 30: Visualisation of the AGB measurements in Gemgemma

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Katna Ruwa is the last exclosure where statistics found a correlation between AGB and the distance to a

street. For this exclosure, no delineation has been made until the present. Besides that, I was not able to visit

this area due to logistic difficulties. These two factors make that I am not able to make a reliable statement

about the founded statistics. In the exclosure itself, only six plots have been made with makes it even harder

to make good interpretations.


In order to investigate the effect of exclosures on groundwater recharge, as mediated through saturated

hydraulic conductivity, undisturbed soil samples were taken in exclosures and if possible, in nearby grazing

land. Data were collected in a total number of 128 plots; in each plot, 2 samples are taken to average out

local disturbances. This means that in total, 256 samples were analyzed in the laboratory. Of these 128 plots,

90 plots were situated in exclosures and 38 plots were situated on twin catchment grazing land. It was not

possible to find suitable grazing land for each exclosure. This has as a consequence that the plots made in

the exclosure were spread out over 10 exclosures and the plots in grazing land were only spread out over 6

areas.

A Mann-Whitney U (=pure statistical) test that compare all the data of the exclosures with all the data of

grazing lands, without incorporating twin catchments or spatial correlations, indicate a statistically significant

difference in the hydraulic conductivity between exclosures and grazing land (Figure 31). Statistics show a

mean permeability of 0.475 cm/s for exclosures and mean permeability of 0.415 cm/s for grazing land. The

difference is significant with a p-value of 0.028. A deeper look into Figure 31 indicates that some plots in

exclosures are having very low values for permeability and that these values are not seen as extreme events.

One possible explanation for these values is that the core sample contained a bigger rock in the center which

was covered by soil on both sides and thus invisible for the researcher. From the data collected, it is

impossible to discern this effect. This rock has a strong influence on the permeability measurements due to

its permeability value of 0. The choice is made to keep these points into the dataset because we are not sure,

and will never be, that the lower values could be caused by bigger rock fractions inside the core.

53

Figure 31: Difference in permeability (cm/s) between exclosures and grazing land when taking into account every collected

data point.

To explain this difference, the hypothesis was made that exclosures consist of lighter topsoil with a lower

bulk density than grazing land. On the one hand, this is due to excluding cattle that compact the soil by

trampling it and on the other hand because of the increased biomass, litter and humus fall that make sure

that the soil contains more soil organic carbon which makes the upper soil layer less dense. To test this

hypothesis, every sample that was analyzed on permeability was also analyzed for bulk density (g/cm²). A

Mann-Whitney U test is showing a statistically significant difference between exclosure and grazing land

(Figure 32). Statistics show a mean bulk density of 1.16 g/cm² for exclosures and mean bulk density of 1.27

g/cm² for grazing land. The difference is significant with a p-value of 4.787*10^-6. A remarkable note that can

be made is that the bulk density of grazing land is still smaller than the bulk density of 1.33 g/cm² as proposed

by Girmay et al. (2009). This could indicate that exclosures are having some buffering effects on the nearby

grazing land or that nearby grazing land is less frequently used by cattle for grazing. One hypothesis to

explain this difference is that farmers are putting their cattle, from time to time, in exclosures to graze and

that this reduces the pressure on the grazing land. This hypothesis asks for a more in-depth analysis of the

behaviour of local farmers and thus, falls outside of the scope of this research.

54

Figure 32: Difference in bulk density (g/cm²) between exclosures and grazing land when taking into account every collected

data point.

At this point, we have the proof that exclosures have a significantly lower bulk density and a significant higher

permeability than grazing land with the same physical characteristics. Statistics that calculate the correlation

between these two variables are showing that bulk density and permeability are significant negative

correlated to each other (Figure 33). With a p-value of 1.17*10^-13, the spearman correlation coefficient is

significant on the 95 percent confidence interval. Based on this figure 33, we see that the datapoints in

exclosures are having high variability in permeability measurements compared to the data points collected

in grazing land. The fact that the regression line between permeability and bulk density is very similar for

exclosures and grazing land, indicates that the higher infiltration rates in exclosure are explained by the lower

bulk density values and not by other consequences (like for example the penetration of roots into the soil that

can break compacted soil layers) that bring exclosures with them.

Figure 33: Correlation between bulk density (g/cm²) and permeability (cm/s) divided per landuse category

55

One form of criticism on these results could be that we take into account all the collected data instead of only

the data that were collected between twin exclosures and grazing land. Another form of criticism that may be

possible is that the collected data have a spatial dimension and that statistical software packages are not

able to take this into consideration. This form of criticism would be justified so in the following two steps, we

repeated the analyses for only the data collected in twin catchment and we looked into the spatial variation

of the results. This last step was executed for each exclosure separately.

By only analyzing data collected in exclosures that have a twin grazing land, the number of plots in exclosures

got reduced from 90 to only 55 plots. The number of plots in grazing land reduces from 38 to 33. We excluded

May’bate from the analysis as the grazing land does not act as a twin catchment (see further). An analysis

of this data indicates that the bulk density in exclosures is still significantly different from the bulk density of

grazing land with a p-value of 0.00015. Statistics indicate a mean bulk density value of 1.18 g/cm² for

exclosures and 1.29 g/cm² for grazing land (Figure 34). Looking at the difference in permeability between

exclosures and grazing land, statistics show that the mean permeability in exclosures is higher than the mean

permeability of grazing land and that the difference is statistical significant. Exclosures are having a mean

permeability of 0.487 cm/s while grazing land has a mean permeability of 0.412 cm/s (Figure 35). The Mann-

Whitney U test produced a p-value of 0.02.

Figure 34: Difference in bulk density (g/cm³) between exclosures and grazing land when only taking into account the data of

twin catchments.

56

Figure 35: Difference in permeability (cm/s) between exclosures and grazing land when only taking into account the data of

twin catchments.

In this next step, the data corresponding to twin catchments was analyzed on the level of exclosures. Table

15 gives a summary of the average permeability and bulk density for every exclosure and grazing land. The

green color indicates the highest value for permeability in every region and the lowest value in bulk density

for every region. The red color indicates the reverse. An examination of this table shows that for every region

except Maybate, the permeability is higher in the exclosure than in the adjacent grazing land. The bulk density

is always lower in the exclosure as compared to the adjacent grazing land. Due to the low number of plots,

it is harder to find statistical support for these statements. Significant differences in permeability between

exclosure and grazing land are only found in May Genet and Tokul. Significant differences in bulk density

between exclosure and grazing land are found in Adilal, Maybate, May Genet and Meam Atali.

57

Table 15: Average permeability and bulk density for twin exclosure and grazing land. The green (red) color indicates the

highest (lowest) permeability and the lowest (highest) bulk density for every region.

* considered as an outlier in twin catchment analyses

** indicates that the difference is significant on the 95 percent confidence interval

Region Landuse Number of

plots Permeability (cm/s)

Bulk density

(g/cm³)

Adilal Exclosure 6 0.54 1.13**

Grazing land 6 0.48 1.19

Lafa Exclosure 6 0.55 1.07


Maybate* Exclosure 12 0.32 1.11**


May Genet Exclosure 8 0.61** 1.09**


Meam Atali Exclosure 15 0.37 1.28**


Togogua Exclosure 12 0.50 1.11


Tokul Exclosure 8 0.48** 1.29


To examine the somehow unexpected higher permeability for the grazing land than for the exclosure in

Maybate, a spatial visualization of the permeability data has been made in Figure 36. We can clearly discern

that the permeability is higher in the eastern part of the exclosure as compared to the western and central

part. The measurements in the grazing lands are all concentrated in the eastern part. Due to its relatively

high permeability, this can give a first explanation on why the average permeability in Maybate is higher in

the grazing land than in the exclosure. Another explanation for the higher permeability rates in grazing land

can be formulated based on the conclusions of Descheemaeker et al., (2006). The exclosure of May’bate is

namely situated on a limestone cliff with basaltic stones and sandstones situated on the higher areas. These

areas act as sediment source areas. Descheemaeker et al., (2006) found out that sediment, coming from the

basaltic and sandstone source areas, has been deposited into the exclosure from the time that it was

established. This resulted in the forming of a mollic horizon with a crumby structure near the surface and an

enrichment of clay particles in the exclosure. This enrichment of clay particles makes that the soil has less

pores and thus that the infiltration rates are smaller. As the token grazing land is situated on the lower

limestone areas without this sediment enrichment, we can conclude that the chosen grazing land does not

58

act as a twin catchment, this is the reason why Maybate was not included in the previous twin catchment

analyses.

The distance between the plots in grazing land and the exclosure is very small. The closest plot in grazing

land is only separated by 108 meters from a plot in the exclosure. To explain the spatial variation in

permeability, one could make the hypothesis that the eastern part is less disturbed by human interferences

due to the larger distance from a village and that there might be an influence of the nearby church forest.

This social impact on the exclosures and they're efficiency are previously examined in paragraph 4.1.5. The

spatial variance in permeability in the exclosure cannot be explained by local differences of the soil class.

The complete area is characterized by loam and more clay loam subsoils. It can, however, be interesting to

note that the lowest permeability measurement is found in clay soil. This statement can not be confirmed by

conducting a Kruskal-Wallis statistic on the relation permeability ~ soil class. No significant correlation is

found when taking into account the complete dataset.

Figure 36: Visualization of the data on permeability measurement in the Maybate region

As one extreme value in permeability has been analyzed, we will now analyze another extreme where there

is a very significant difference in permeability between exclosure and grazing land. For this, the exclosure of

Tokul is taken. Results of the permeability measurements for both the exclosure and the grazing land are

visualized in Figure 37. The distance between the plots of the exclosure and the grazing land is at least 1000

59

meter. The distance between the nearest village and the exclosure or grazing land plots is also at least 1000

meter. The exclosure is situated somehow in the middle between four nearby villages, this indicates that the

assumption can be made that human interference in the exclosure has no clear spatial dimension. The

grazing land has only one nearby village. Based on the background image of Figure 37, the greener color of

the exclosure indicates the denser vegetation and we can also see that the plots taken in the exclosure and

grazing land are influenced by the same physical structures. This ensures that the grazing land is a very

representative area to act as a twin catchment for the exclosure. The results are showing permeability values

for the exclosure that are all higher than 0.38 cm/s while for the grazing land, the opposite is true. Here,

permeability measurements are not exceeding a value of 0.38 cm/s. This region clearly indicates the effect

of an exclosure on the saturated hydraulic conductivity of the topsoil.

Figure 37: Visualization of the data on permeability measurement in the Tokul region

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All the results on permeability measurements, including the results from Maybate and Tokul, are showcased

in Figure 38. Taking a deeper look into the exclosure of Meam Atali, a spatial correlation on permeability

between the different plots can be distinguished. This observation is simultaneously a good quality check for

the work conducted by the laboratory technicians of Mekelle University. The highest permeability values are

found in May Genet and Gemgemma, the lowest in Maybate.

Figure 38: Visualization of the complete dataset on permeability measurements in the Dogu’a Tembien region

Based on all the results in the three different levels (complete dataset, twin catchment analyses, level of

exclosures). We are able to make the grounded conclusion that exclosures are having higher permeability

rates and lower bulk density values than adjacent grazing land. It is by excluding cattle from exclosure that

the soil is less trampled and thus less compacted.

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As last, we analyze if there is a link between SOC and the permeability of the soil. A point cloud of the data

is visualized in Figure 39. Statistics did not found a significant correlation between these two variables. This

makes that our hypothesis on the link between SOC and permeability is proven to be invalid.

Figure 39: Correlation between SOC (%) and permeability (cm/s)

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5. CONCLUSION


Exclosures in the Dogu’a Tembien region store carbon in two major ways. The first one is in the form of living

biomass while the second one is the sequestration as soil organic carbon. Our results show that the soil

captures up to 10 times more carbon than living biomass. It is of importance to find the driving factors behind

the regenerating process of living biomass as this forms the input for SOC. We analysed the presence and

success of biomass to climatic conditions, soil characteristics, site conditions, human impact and time since

the establishment. The major conclusions are summarized in Table 16.

Table 16: Overview of the major carbon sequestration influencers

* Only for statistics comparing continues data, not shown for categorical data

Category Level Variables Correlation R²-value * p-value

Age

Exclosure Biomass ~ Age positive 0.49 0.0233

Plot AGB ~ Category Age positive 0.00017

Plot SOC ~ Category Age divided 0.00955

Soil

Plot AGB ~ % silt Negative 0.106 0.00151

Plot AGB ~ Soil class Positive with clay 0.04861

Plot SOC ~ % clay Positive 0.15 0.00238

Human

Plot AGB ~ Grazing

pressure

Negative 0.02594

Plot AGB ~ Distance to

village

Positive 0.03 0.03043

Plot AGB ~ Distance to

street

Positive 0.16 4.49*10^-6

We see that the major determinant of biomass regeneration is the time variable, times explains up to fifty

percent of the inter exclosure variability in AGB. This is a passive variable as all biomass regeneration

projects have to undergo this process. Besides this variable, the two other factors that drive variability are

the influence of the soil and the influence of human impact. Our table shows that based on the relative high

R² and the very low p-value between AGB and distance to the street, the influence of human impact exceeds

the influence of soil variability. We conclude that plots and exclosures which are more isolated from the

human world have higher AGB values as humans are still using exclosures as illegal grazing and tree cutting

land. These observations make us say that Age and human interferences are the dominant driving factors

behind inter- and intra- exclosure variability of AGB. The impact of soil characteristics on AGB are related to

the local climate as biomass receives high moisture and water stress levels. The lower the silt and the higher

63

the clay concentrations in the soil, the more water that gets trapped during rain events that is available for

the biomass.

The results for SOC are less obvious as statistics found a linear correlation between SOC concentrations

and the age of a plot. Statistic did however found significant higher values for SOC in the youngest and oldest

exclosures. The higher SOC concentration in the oldest exclosure is purely explained by the passed time.

The higher SOC concentrations in the youngest exclosure are statistically still unexplained. There is only a

strong suspect that this is explained by the management. In contrast to AGB, human interferences do not

have a direct impact on the SOC concentrations.

Climatic variability and site conditions do not affect the biomass regeneration / carbon sequestration process

in our study area as no correlations have been found.


Statistics prove that exclosures are having significantly higher permeability values and lower bulk density

values compared to adjacent grazing land (p-value: 0.0206). This is confirmed for working with all the

collected data and for only using the data that are integrated into a twin catchment. Every exclosure also had

higher (lower) mean values for permeability (bulk density) compared to its twin grazing land. On this level,

the correlation is only significant for some exclosures. The higher infiltration rates in exclosures are direct

linked to the lower bulk densities. The lower bulk densities in exclosures are explained by the exclusion of

cattle. This exclusion makes that cattle no longer compact the soil by trapping on it. The infiltration rates of

exclosures can not be linked with the increased amount of SOC as there is no correlation between these

variables.

6. DISCUSSION

6.1. Method

The allometric equation used to measure the biomass in an exclosure does not affect the conclusion on

carbon sequestration. However, what is important in our method is that we make the assumption that the

initial biomass levels in an exclosure at the time of establishment are similar. We are not able to confirm this

statement as we do not have historical biomass measurements of these areas. It is hard to believe that all

the exclosures have the same initial conditions so we can only assume that there is a presence of some

inter- (and even intra) exclosure variability on the initial biomass levels. This initial variability makes in not

only for the age but for all the other explaining factors as well, more difficult to gain insight into the real

dynamics of an exclosure. More insight into the dynamics and even more reliable statistics (possibly with

higher R²-values) would be obtained when repeating this same study in 5 to 10 years and to work with the

differences in biomass concentrations and SOC.

64

Another point of discussion is the limitation in time and resources to collect data. During the intensive two

months of fieldwork, a tight schedule had to be made in order to ensure that all the necessary work was

completed. This schedule made that we could not exceed the timeframe of one week to measure biomass in

new exclosures. When an exclosure thereby is also difficult to access from the basecamp, the actual time in

the exclosure got reduced to a maximum of four days. This makes that for big exclosures like Adilal, the

number of plots measured are an underrepresentation for the exclosure. The few plots are as a result of the

used method also very concentrated in one part of the exclosure. A better technique to use would have been

the method of reduced sampling strategy where we double the amount of distance between two consecutive

plots. This method would allow us to find more insight into the intra-exclosure variability of biomass levels.


Measurements on carbon sequestrations showed very high inter and intra exclosure variability. We were not

able to explain all these variability levels. Some intra exclosure variability is due to human interferences as

proved in paragraph 4.1.5. More variability could be explained by initial variability in biomass levels. This is

definitely true in some cases where cropland was abandoned at the time the exclosure was established. No

information on the field was collected for these areas which make that these plots cannot be distinguished in

the dataset. Some variability can also be explained by the used method to calculate the total amount of

biomass in a plot. The total amount of biomass in a plot is namely the biomass of the 20x20 plot plus 16

times the biomass of the 5x5 plot. In this way, a small Acacia etbaica in the 5x5 plot could create an additional

100kg of biomass in the total plot. It is thus clear that this method also produces some additional variability

with sometimes an over and sometimes and underestimations real biomass values.

6.2.1. Management

Statistical analyses on the used dataset were not able to prove the effect of management on SOC

concentrations. We do however have the feeling that it is the presence of extensive management structures

in the youngest exclosures that explain the significant high SOC concentrations. The used dataset has a lack

of exclosure with the same age where some exclosures are under influence of management while the others

are not. This lack ensures that one on one correlation tests to examine the effect of management are

impossible. We are only able to compare younger exclosure with management to older exclosure without

management. Due to the strong positive correlation between the age of an exclosure and the amount of

carbon sequestered, a supposed link between SOC and management may be suppressed. The data

collection strategy was not build to compare purely the effect of the management.

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6.2.2. Climatic conditions

Vegetation in our study area is under influence of water stress, this makes that we expected higher biomass

measurements in exclosures that receive more rain or even fewer direct solar radiation and thus lower

temperatures. Statistics were not able to confirm this assumption. We discuss this result by arguing that the

used dataset is not conform to our expected resolution, the dataset is also generalised for the complete global

world and thus not focussed on our study area. The availability of a precise climatological dataset that is

zoomed in into our study area that takes for example rain shadow into considerations, could provide

additional results for this study. Unfortunately, as far as our knowledge goes, no such dataset is freely

accessible.

6.2.3. Soil texture

The statistics found a negative correlation between AGB and the silt fraction of the soil. No direct correlation

was found between AGB and the clay fraction, we would however expect a positive correlation between

these variables. Indirectly, by using soil classes, we did however see that the more clayey soil classes had

significant higher biomass levels. We can produce two hypotheses for these results. The first one is purely

based on the silt loamy soils. We namely know that silt loamy soils are more vulnerable to erosion (Middleton,

1930). This makes that soils with higher silt proportions might have more total erosion levels. This erosion

has the capacity to trap nutrient-rich particles and to remove them from the exclosure. This deficit in nutrient

could then be the explanation for the lower biomass levels. From all visited plots, an indication has been

made on signs of erosion. We did however never see an active form of erosion in an exclosure. The tricky

part here is that sheet erosion can be very difficult to observe. This hypothesis can only be confirmed by

doing chemical analyses on the soil to link the amount of nutrient with the amount of silt of a soil. Based on

the collected dataset, no clear explanation can be given for this correlation.

The second explanation is supported by common sense. Here, we apply circular reasoning: a significant

negative correlation between AGB ~ % silt and a significant negative correlation between %silt ~ %clay

results in a positive correlation between AGB ~ % clay. This result is more in line with the fact that vegetation

receives water stress, that the more clayey soils capture more water with almost automatically results in a

reduction of water stress and thus an increase in biomass. This assumption would also be in line with the

results of Mekuria et al., (2011). Additionally, it is known that clayey soils have a higher cation exchange

capacity which is also a good indication for the soil fertility (Hazelton et al., 2007)

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6.2.4. Terrain characteristics

In the result section on terrain characteristics, no in-depth analyses were done on the correlation between

SOC and the biodiversity despite the produced significant correlation by the statistics. This because I

questioned the causality of the relationship between these two variables. We namely found that AGB is

positively correlated to SOC and that over time with the aging of exclosures and the increasing numbers of

AGB. The amount of species in an exclosure, as the exclosure evolves into a next ecological succession

phase, also develop as pioneer species are coming in competition with more climax species. For this reason,

this is not included in the paragraph on results.

In this same paragraph, we also left out the very significant negative correlation between AGB and the

elevation of a plot (p-value: 4.083*10^-5). This because the correlation is only due to the differences in

elevation of each exclosure (Figure 40). Analyses within one exclosure between these variables produces

no clear trends or correlations. The variability in elevation within one exclosure was most of the time also

very limited.

Figure 40: Data cloud of the correlation between AGB and elevations

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6.2.5. Human interferences

The statistics found that humans interferences into an exclosure are negatively impacting the biomass

concentrations. We found correlations between AGB and grazing pressure, AGB and distance to a major

street and as last, AGB and distance to the village. On the level of exclosure, we found a correlation for

Gemgemma, Katna Ruwa, Meam Atali and Togogua between AGB and the distance to a street. More

interesting in these statistics is that we did not find significant results for exclosures that are very closely

situated near the streets. For this, we would like to indicate the exclosure of May Genet, Afedena and Tokul.

We first focus on the exclosure of May genet. This exclosure differentiates itself from other exclosures by the

simple fact that the exclosure does not consist of one consecutive area but that exclosure and cropland

alternate each other. The measurements on AGB in May Genet are represented in Figure 41. We can clearly

distinguish the three transects in the exclosure that are separated from each other by cropland. Despite that

statistics are not indicating a correlation between AGB measurements and the distance to the road, a visual

interpretation of the exact AGB values indicates that there seems to be some relation. The densest

vegetations seem to occur the further away from the road. In this specific case, a correlation would make

sense because farmers need to reach their cropland that is situated within the exclosure. The farmers are

living in a nearby village that is situated closely to the road. This makes that farmers are approaching their

cropland from the road. This on his turn makes that transect one of the exclosure needs to be crossed by the

farmers who’s cropland is situated between following transects: 1-2, 2-3 and 2-3 again. This makes 3 crosses

by cattle in total. In contrast to transect one, transect two only needs to be passed 2 times and transect three

doesn’t have passage at all. It is only logical that cattle eat some of the vegetation of the exclosure when

passing it. This story gets confirmed by the grazing pressure numbers. Transect one and two are having over

their total length a grazing pressure number of three while transect four is having a 50/50 distribution of

grazing pressure numbers of three and four. This case is again showing the impact of humans on the biomass

regeneration. A counter argument for this biomass variation between the transects can be that the cropland

has an effect of the soil water availability in the exclosure. A schematic explanation is given in Figure 42. We

see that the croplands in May Genet are situated on the flat surfaces while the exclosure is situated on the

steeper parts. The farmer ploughs his cropland, under normal circumstances, at least once every year. This

ploughing makes that the structure of the topsoil is broken so that water and air infiltrate more easily. This

additional water in the soil will be transported downwards into all the directions. This makes that also the

subsurface of the exclosure contains more water and thus that the living biomass receives less water stress.

We can now argue that transect 3 of the exclosure has, relatively, the lowest water stress as it receives

additional water from all the above croplands. Transect one has relatively the highest water stress as it

receives only additional water from one cropland. This additional water availability could thus also play a role

in the differences in biomass between the three transects. This statement remains however a hypothesis,

more research on moisture content in deeper ground layers for this area is needed.

68

Figure 41: Visualisation of the AGB measurements in May Genet, exact AGB values are labelled on every plot.

Figure 42: 3D schematic view of the exclosure of May Genet, distances are not scaled

69

The exclosure of Afedena is literally situated next to the road, this makes it directly the most easily accessible

exclosure of all. The exclosure itself has a lot of management going on and consist of a steeper part with a

slope around 30 percent where transect one has been taken on a more flat part with slopes around 20 percent

where data for transect two was collected. In the exclosure, numerous walking paths were visible in

combination with grazing pressure marks. This makes that all the plots in Afedena obtained a grazing

pressure value of two or three. The AGB measurements of every plot are visualised in Figure 43. For this

exclosure, no correlation has been found between AGB and the distance to the street. Even stronger, the

plots with the highest amount of AGB are the plots that are the closest to the street. We could make the

hypothesis for this exclosure that the presence of the main street by itself is not enough to impact the biomass

regenerating process. It is also the absence of main villages along the asphalt road that makes that this part

of the street is only limited walked by cattle. The closest village in the NW is over 2.5 km removed while in

the SE, the distance even goes up to 3 km. The nearest village can be found one kilometre northward from

the exclosure on the other side of the big gully. The absence of the correlation between AGB and distance

to the street could thus be explained by the fact that every part of the exclosure is very easily accessible for

man and cattle which makes that no parts are more influenced by tree cutting and grazing than other.

Figure 43: Visualisation of the AGB measurements in Afedena, exact AGB values are labelled on every plot

70

The last exclosure that is situated close to a major street and where the statistics did not find a correlation

between AGB and the distance to the street, is the exclosure of Tokul. AGB values of this exclosure are

visualised in Figure 44. The main asphalt road is situated southwestern of the exclosure and is hidden

(unfortunately) by the legend of the map. The map indicates that the distance to the major asphalt road or

the rural road does not affect the AGB. This is in line with my personal field experience. In this exclosure, it

seems like it is the topographic shape of the exclosure that is responsible for variations in biomass. The

exclosure is namely situated in an almost circular water basin with the most central points of the exclosure

situated in the centre of the basin. It is on these points where the highest AGB values are measured. This

assumption is in line with the previous results that state that the biomass regeneration process is influenced

by water stress levels. This basin makes that the most central parts of the exclosure are not only having the

water available from direct rainfall but also from overland and subsurface run-off. These higher levels of water

availability in this area could thus explain the higher AGB measurement. Based on this, we could run a

statistical analysis between the size of the watershed in every plot and AGB values. Despite the available

digital elevation model (DEM) of the Dogua Tembien region, a precise small-scale calculation of the

watershed area for each plot in ArcMap is not possible as the DEM has a resolution of only 30 meters.

Based on my personal field experience, I can also tell that the vegetation in the most central plots was strongly

dominated by Acacia etbaica and that this Acacia etbaica was forming a closed canopy cover on +/- 2-meter

height. This makes that the ecosystem will slowly change towards a more forestry ecosystem with an

adjusted microclimate (less direct incoming radiation, more temperate temperatures and higher relative

humidity). This microclimate could than result in a positive feedback loop where new biomass regenerate

faster. This is however only a hypothesis; more research is needed to confirm or deny this statement.

71

Figure 44: Visualisation of the AGB measurements in Tokul

6.3. Permeability + bulk density

Soil samples for permeability and bulk density measurements where collected in metal, opaque, cores that

were made available by the university of Mekelle. This method has its pros and cons. The biggest advantage

was that data collections was rather easy, limited in time on the field and qualitative good. The biggest

disadvantage is that you are dependent on the university and thus limited in resources. Most of the time, not

enough cores were available at the laboratory. This made that we had to make clever choices on the field on

where to collect our data and where not. The limitation in materials is the main reason why the data collection

in grazing land is so limited. Luckily for us, this limitation just stayed within the limits to not have a severe

impact on the results. If more cores would have been available, the all the statistics on bulk density and

permeability would probably have produced more significant correlations. It is also possible that more

correlations would have been found on the level of exclosures.

72

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Meteorological data Hagere Selam: National Meteorological Agency (NMA)

Date post:	24-Jan-2022
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BENEFITS OF REFORESTATION ON CARBON STORAGE AND …

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