i

INVESTIGATING THE INFLUENCE OF CLIMATE

VARIABILITY ON BARK STRIPPABILITY IN BLACK

WATTLE (Acacia mearnsii) PLANTATIONS IN CHIMANIMANI

WATTLE MIMOSA ESTATES

MAGURANYANGA. MIKE

N009 4477X

SUPERVISOR: A.GUMBI

MAY 2013

A thesis submitted in partial fulfilment for the requirements of Bachelor’s Degree Hons

in Forest Resources and Wildlife Management Department of Forest Resources and

Wildlife Management Faculty of Applied Science National University of Science and

Technology (N.U.S.T), Bulawayo

i

APPENDIX II

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APPENDIX III

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The undersigned certify that they have read and recommended to the National University

of Science and Technology for acceptance: a dissertation entitled “Investigating the

influence of climate variability on bark strippability in black wattle (Acacia mearnsii)

plantations of Chimanimani Wattle Mimosa Estates”, submitted by in partial fulfilment of

the requirements for the Degree of Bachelor of Environmental Science Honors in Forest

Resources and Wildlife Management

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iii

ABSTRACT

There has been some fluctuations in the climatic (precipitation, temperature and rain

days) and a decrease in bark strippability. Secondary data from 1999 – 2012 (Nov – Mar)

obtained from Wattle Planning Department was used for analysis. Incomplete

randomized block design was used for data collection (see experimental design). Changes

in climatic variables and strippability variables were analyzed using one way Anova

while bivariate correlation was used to test for the relationship between the variables and

strippability using SPSS version 21. Precipitation and rain days did not significantly

change (P>0.05) but had a significant positive correlation of 71.5% and 42.9%

respectively with strippability. Temperature had significant increases (P<0.01) while

strippability had significantly decreased (P<0.01). Temperature is significantly negatively

correlated with strippability (27.7%). Results show that climatic variables have

significantly changed over the 13 harvesting seasons with rainfall showing a greater

influence on bark strippability while temperature has the least effect. It can be concluded

that climate variability is the major factor that has resulted in the decline in yields and

strippability. Therefore it can be suggested that drought and temperature resistant seeds

be used for establishment, a tree breeding program be initiated to reduce establishment

cost, to allocate more labor units to stripping during harvesting period to meet targets

while the rains are still available. Also more studies in other estates covering long periods

can be done to ascertain the new trends so as to enable informed decisions on either

shifting the season in accordance to the new trends.

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ACKNOWLEDGEMENTS

I would like to extend my sincere appreciation to the Wattle Company for allowing me to

carry out my research and all the resources they supported me with during the execution

of this project. I would also want to thank Mr.J.Chamanwa, Mr.W.Chikukwa and

Mr.W.Zengeya from the Wattle Mimosa Planning Department in Chimanimani. They put

tremendous efforts in helping me to acquire the data and records for bark strippability,

temperature and rainfall for my study area. All their technical expertise and insightful

comments are greatly appreciated.

My appreciation further extends to my supervisor Mrs.A.Chichinye for her guidance

throughout the whole project. I thank would also like to thank Mr.H.G.T. Ndagurwa,

Mr.A.Magwizi, Mr. Mwase and Mr.T.Nzuma for their comments and contributions in

shaping up this piece of work.

Many thanks to my family members for their support and encouragement. Special

mention goes to my father Mr.E.Maguranyanga for his financial support in paying my

fees. My aunt Ms.E.F.Maguranyanga for her shared views and opinions about my

research and my fellow friend Mr.O.Sonono for his assistance in structuring my project.

Above it all l would like to thank God Almighty for his undying love towards me.

”Ebenezer”

v

DEDICATION

I dedicate this art of master piece to my mentor my father Mr.E.Maguranyanga who has

been financially supporting me. Blessed be his fortunes.

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Contents TITLE PAGE

Release form i

Approval form ii

Abstract iii

Acknowledgements iv

Dedication v

Contents vi

List of figures vii

List of tables viii

List of appendices ix

CHAPTER ONE ................................................................................................................. 1

INTRODUCTION .......................................................................................................... 1

1.1 Introduction ...................................................................................................... 1

1.1.1 Background to the study ............................................................................... 3

1.2 Problem statement ............................................................................................ 6

1.3 Broad objective ................................................................................................. 7

1.4 Specific objectives ............................................................................................ 7

1.5 Research questions ........................................................................................... 7

1.6 Research hypothesis ......................................................................................... 8

1.7 Limitation ......................................................................................................... 8

1.8 Justification ....................................................................................................... 8

1.9 Assumptions ..................................................................................................... 9

1.10 Scope (delimitation) of the study .................................................................... 10

1.11 Definition of terms ...................................................................................... 10

1.12 Project layout .............................................................................................. 10

CHAPTER TWO .............................................................................................................. 12

LITERATURE REVIEW ............................................................................................. 12

2.1 Introduction .................................................................................................... 12

2.2 Documented researches .................................................................................. 12

2.3 The felt impacts of climate variability ............................................................ 13

2.4 Climate variability and forest ecosystems ...................................................... 14

2.5 Influence of climate variation impacts on forest productivity ........................ 16

2.6 Summary ......................................................................................................... 20

CHAPTER III ................................................................................................................... 22

METHODOLOGY ....................................................................................................... 22

3.1 Introduction. ................................................................................................... 22

3.2 Study Area ...................................................................................................... 22

3.2.1 Location ...................................................................................................... 22

3.2.2 Vegetation ................................................................................................... 23

3.2.3 Geology and soils ........................................................................................ 24

3.2.4 Climate ........................................................................................................ 24

3.2.5 Relief ........................................................................................................... 25

3.3 Materials and methods .................................................................................... 26

vii

3.3.1 Experimental design.................................................................................... 26

3.3.2 Data collection ................................................................................................ 27

3.3.3 Precipitation ................................................................................................ 27

3.3.4 Bark strippability ........................................................................................ 28

3.3.5 Temperature .................................................................................................... 28

3.3.6 Data analysis ................................................................................................... 29

CHAPTER IV ................................................................................................................... 30

RESULTS ..................................................................................................................... 30

4.1 Introduction .................................................................................................... 30

4.2 Precipitation trends ......................................................................................... 31

4.3 Temperature trends ......................................................................................... 32

4.4 Bark strippability trends. ................................................................................... 33

4.5 Rain days ..................................................................................................... 34

4.6 Changes in the mean monthly rainfalls. ...................................................... 35

4.7 Correlation between climatic variables and bark strippability. .................. 36

4.8 The correlation between mean seasonal rainfall and bark strippability. ..... 37

4.9 Correlation between mean seasonal temperature and bark strippability .... 38

4.10 Correlation between mean seasonal rain days and bark strippability. ........ 39

CHAPTER 5 ..................................................................................................................... 40

DISCUSSION ............................................................................................................... 40

5.1 Trends in climatic variables ............................................................................ 40

5.1.2 Temperature ................................................................................................ 40

5.1.3 Rainfall. ....................................................................................................... 42

5.1.4 Rainfall days ............................................................................................... 43

5.1.5 Strippability................................................................................................. 44

CHAPTER 6 ..................................................................................................................... 45

CONCLUSSIONS AND RECOMMENDATIONS ................................................. 45

6.1 Conclusions .................................................................................................... 45

6.2 Recommendations ........................................................................................... 46

6.3 Suggestions ..................................................................................................... 46

REFERENCES ................................................................................................................. 47

APPENDICES …………………………………………………………………….…… 55

Appendix 1; Research data collected ………………………………………………55

Appendix 2; Anova table for differences in climatic variables and strippability…..57

Appendix 3; Pearson's correlation table of climatic variables and bark strippability57

Appendix 4; Mean (SE) of climatic variables and bark strippability …………….. 58

viii

LIST OF FIGURES

Figure 1; Location of the study area in Zimbabwe ………………………………...…. 23

Figure 2; Location of the study area in Chimanimani (Silverstreams) …………...……24

Figure 3; Changes in mean seasonal monthly rainfall (1999 – 2012) ……………...…. 26

Figure 4; Experimental design used for the 2011/2012 study period …...…………….. 27

Figure 5; Graph showing mean seasonal rainfall for Chimanimani (1999 – 2012) ...…. 32

Figure 6; Graph showing mean seasonal temperature for Chimanimani (1999 – 2012)..33

Figure 7; Graph showing mean seasonal bark strippability in Chimanimani ...……….. 34

Figure 8; Graph showing mean seasonal rain days in Chimanimani (1999 – 2012)…... 35

Figure 9; Graph showing correlation between rainfall and bark strippability ...………. 38

Figure 10; Graph showing correlation between temperature and bark strippability ...… 39

Figure 11; Graph showing correlation between rain days and bark strippability ...…… 40

ix

LIST OF TABLES

Table 1; Mean seasonal monthly rainfall in Chimanimani Wattle Mimosa Plantations...35

Table 2; Anova correlation table of climatic variable and strippability ………………...36

x

LIST OF APPENDICES

Appendix 1; Research data collected ……………………………………………55

Appendix 2; Anova table for differences in climatic variables and strippability .57

Appendix 3; Pearson’s correlation table of climatic variables and strippability ..57

Appendix 4; Mean (SE) of climatic variables and bark strippability ………….. 58

1

CHAPTER I

INTRODUCTION

1.1 Introduction

The tree plantation industry is vulnerable to climate variability since it is a plant- based

industry and generally these plants respond sensitively to climate and atmospheric

conditions. Area climatically suitable for exotic tree plantations in Zimbabwe is

extremely limited and subject to other land and water pressures and increase in climatic

variation effects have posed a further threat. Tree cropping has a long planning horizon

(± 9years - ± 25 years), a long period between the commitment of investment and the

realization of profits and high sensitivity to transport cost. Therefore the decreases in

yields and forest productivity as a result of erratic rainfall patterns, increase in

temperatures and dry spell have exacerbated to the rate of loses incurred in the timber

industry. Therefore there is need to understand how climatic patterns particularly in

Zimbabwe have changed so as to understood how it has influenced various sectors of

production in the country.

Forest plantations account for at least 110 000ha of the total forest area in Zimbabwe

grown under the exotic species such as the Pinus, Acacias and Eucalypts (Melillo et al.,

1990). This area is threatened by the impacts of climate change as vegetation has shifted,

boundaries reduced and land productive capacity negative impacted by variations in

2

temperature and rainfall. These two have been noted to be the important factors

influencing the metabolic processes and growth in plants (Jensen, 2000).

In Zimbabwe, Wattle Mimosa is the only company in involved in the production of

tannin extract from Acacia mearnsii (Sherry, 1971). The plantations of these exotic are

found in the Eastern Highlands where there is sufficient rainfall received evenly across

the rain season thus making it suitable for forestry practices (Gwaze, 1986). Within the

period of climate change speculations, the rainfall has become unpredictable and reduced

while temperatures and dry spell increased. This is thought to have negatively impacted

on productivity as there has been a notable decline in bark yields and strippability.

A number of sectors contribute to the country’s GDP through export of products and

Forestry timber industry is part of the list in Zimbabwe. The exports list includes raw

materials, unprocessed and finished timber products which are taken to neighboring

SADC countries and across the seas (Shumba, 2006). Transformation to local livelihoods

has been marked as one of the major contributions by forests to communities through

provision of timber and non-timber products as well as forest services. In India the

popularity and earnings from forestry plantations have made this sub-sector an area of

interest to policy makers (UCL, 2009).

Globally, there is a rise in concern over the felt effects of climate change and their

impacts on a number of sectors including agriculture, forestry, health, wildlife and

environment. Forests are an essential component in the ecosystem as a result of their

roles and services in conserving biodiversity and acting as carbon sinks in carbon

sequestration. Climate change has resulted in erratic rainfall patterns, increased

3

temperatures and dry spell duration. IPCC (2007) reported 0.6ºC ± 0.2º C increase in

global temperatures while Hulme et al. (2000) predicted a 5% decrease in precipitation

and 15% of the Southern Africa becoming drier. This would affect forests and their

productivity as they are sensitive to slight changes in the climatic variables in particular

precipitation and temperatures (Jensen, 2000). Over the past century these climatic

factors have tremendously been fluctuating thus resulting in changes in the forest

structure and health.

IPCC (2007) defined climate variability as any change in weather patterns over time (<30

years) due to natural variability or human activity. These activities include industrial

activities, power generation, agriculture, waste emissions, deforestation, veld fires and

urbanization. Such activities result in increased greenhouse gases (CO2, CH4, O3 and

NOx) which give rise to global warming (IPCC, 1996). The changes observed over the

last several decades are due to human activities, but it cannot be ruled out that some

significant part of these changes is also a reflection of natural variability. (NRC, 2001).

1.1.1 Background to the study

Indigenous forests and exotic plantations have become common in the country as they act

as the carbon sinks of Zimbabwe and the ability of creating employment opportunities to

the local communities (Unganai, 2009). It is estimated that 70 percent of the adults in

these areas are all employed in the plantations therefore the need to maintain forest

productivity for forest sustainability (Melillo et al., 1990). This would secure the source

of their livelihoods thus reducing poaching and illegal activities and conflicts of interest.

4

Forest product exports accounts for at least 1% of the country’s economic output and this

explains why regionally and locally the forestry sector has be given priority. Although the

size of forestry sector has dwindled together with the manufacturing sector due to climate

and financial constraints respectively, they remain the key sectors of economic growth

(Shumba, 2006).

Forests and plantations offer several products and services to livelihood (Clarke and

Katerere, 1994) but this report considered only the marketed products. Wattle Mimosa

concentrate on A.mearnsii bark production for tannin extraction. The wood from the

stripped stem is then used to produce charcoal which is also sold locally as bulk charcoal.

Quality vista charcoal is exported to South Africa, Mozambique and Zambia. Black

wattle species is of importance in the tannin industry as it is a natural source of high

tannin concentration (Adeleke and Goh, 1980). It was introduced from Australia by CJ

Rhodes in 1902 (Sherry, 1971). The first trials were done near Matobo but they gained

prominence rapidly in the Eastern Highlands by 1934 making Zimbabwe part of the

countries that owned large area of black wattle plantations (Sherry, 1971). Other

stakeholders in the industry majors on other species such as Pinus species for timber and

Eucalypts species for poles.

The major threat that has a cause of concern to the foresters are the fluctuations and

variations in the climatic variables which are being experienced on short cycles (IPCC,

2007). Matarira and Mwamuka, 1995 reported an increase in temperatures by 0.4°C and

drop in rainfall by at least 10% in Zimbabwe has already posed a threat to productivity in

various sectors thus the need for researches to understand the phenomenon better. In

other areas rainfall has further reduced to as little as 30 percent of the annual total rainfall

5

(Orlove and Tosteson, 1999) and drier areas have increased by 15 percent (Mugandani et

al., 2012)

Bazzaz (1990) reported the sensitivity of forests to slight changes in climatic variables so

it has been thought that the decrease in the bark yields and productivity in Chimanimani

could be a true reflection of climate variability. Increases in temperatures and low

standard rainfall periods leads to reduced soil moisture content thus lowering bark

strippability. Due to erratic rainfalls, trees have been noted to attain thin diameters and if

these thin diameter trees increase per unit area, they will definitely reduce total yield

(Gunduz et al., 2011). Kadonhera (2011) unpublished, reports that productivity have

dropped from 16 tons/ha to 13tons/ha. The decline is a result of multi-factors with

climate variability being listed as the major factor (Gunduz et al., 2011).

The Afromontane typical vegetation of the Eastern Highlands is feared to be reduced to

bush lands as the impacts of climate change and variability are projected to have

significantly increased by 2030 (IPCC, 2007). The region will lose its vegetation

structure and there might be need for reclassification of the agro-ecological regions

(Mugandani et al., 2012). Phenological changes have been reported such as early timing

of spring events such as leaf unfolding, leaf flowering, species migration and range shifts

(Leith, 1974). This is because quite small changes in climatic variables have detrimentous

effects on the viability of plants (Jensen, 2000).

To produce a standard product on expiration of the rotation time, it requires forest

processes and resources which can be natural or man-made (Geldenhuys, 1981). Essential

natural resources for black wattle production include land, precipitation, sunshine, air and

6

temperature. Man-made resources includes labour, capital, management and

entrepreneurs (Ziervogel et al., 2008). Climate has been identified as the predominant

factor that influences land productive capacity including bark yields (UCL, 2009).

Although studies have shown that forests have adapted to temperature increases (2-3ºC)

in the past, these changes occurred over thousands of years. Current climate predictions

suggest that average global mean temperatures could rise by (1.5-5.8°C) over this century

alone. Such rapid changes in a relatively short period of time could affect forests

significantly (Jensen, 2000).

The on-going trends in climate variability with speculations of further increase in the

negative impacts of climate change by 2050 therefore pose a threat to the forest

productivity and health (Gunduz et al., 2011). Zhakata (2010) acknowledges the fact that

yields have been reduced in crops as a result of this phenomenon of climate variability. It

has led to increased frequency of droughts, floods, prolonged dry spell duration and

unpredicted rainfall patterns (Mugandani et al., 2012)). However studies on climate

change have generally been focusing on climate variability on agriculture and how this

would impact on livelihoods. There is paucity of literature on specific forest products and

how they have been influenced by this variability. This work seeks to investigate the

influence of climate variability on A.mearnsii bark strippability in Chimanimani black

wattle plantations.

1.2 Problem statement

There was a notable decrease in bark strippability and fluctuations in the climatic

variables from the data available for 1999 – 2012. Bark yields have been noted to have

7

declined from 16tons/ha to 13tons/ha while bark strippability have reduced from (85% -

59%) in Chimanimani (Kadonhera 2011, unpublished). No studies have been carried out

to ascertain how fluctuations in climatic variables have affected the growth of A.mearnsii.

1.3 Broad objective

To establish the changes in climatic variables and how they have been influencing

A.mearnsii bark strippability in Chimanimani Wattle Estates over the 1999/00 – 2011/12

stripping seasons.

1.4 Specific objectives

To determine if there are any significant changes in climatic variables

(temperature, rainfall and rain days).

To establish if there are any significant changes in bark strippability over the

1999/00 – 2011/12 stripping seasons.

To determine if there is any correlation between climatic variables and bark

strippability over the 1999/00 – 2011/12 stripping seasons.

1.5 Research questions

Are there any significant changes in the climatic variables (rainfall, temperatures

and rain days) over the 1999/00 – 2011/12 stripping season in Chimanimani?

Is there any significant change in bark strippability over the 1999/00 – 2011/12

stripping seasons in Chimanimani?

Is there any correlation between climatic variables and bark strippability?

The above research questions are to provide answers for the mentioned objectives.

8

1.6 Research hypothesis

H0 - there are no significant changes in climatic variables (rainfall, temperature

and rain days) over the 1999/00 – 2011/12 stripping season stud in Chimanimani.

H0– there is no significant change in the bark strippability over the 1999/00 –

2011/12 stripping season in Chimanimani

H0 - there is no correlation between climatic variables and bark strippability over

the 1999/00 – 2011/12 stripping season stud in Chimanimani.

1.7 Limitation

Some of the data records were in tatters thus information recorded was not vivid

and eligible.

Some records could not be accessed thus this shortened the study period confining

it to 1999/00 – 2011/12.

1.8 Justification

As this phenomenon of climate change is still poorly understood, few studies have been

done to ascertain the extent to which productivity has been influenced by climate

variability in forests and timber plantations. Greater population of the surrounding locales

in Chimanimani depend on the plantations for a living (employment). So a study of this

nature will depict a clear picture of how much production have been negatively

influenced by climate variability thereby helping to predict the future. This then allows

short term and long term planning for mitigation measures by the management for

sustainability. It reduces poverty levels in that area through continuity of employment as

well as other forest services provided by the forests.

9

The extent to which woodlands and plantations are being influenced by climate

variability is poorly understood in Zimbabwe. Most studies have concentrated on

agricultural crops and impacts on livelihood, so this research aims to provide information

on how climatic variables have been fluctuating and impacting on bark strippability. The

results of this project can be useful to the Timber Producers Federation and all the

stakeholders. This will enable the management to strategize and make informed decisions

to ensure sustainability.

The research is also presumed to immensely contribute towards enrichment of baseline

information on how climatic variables have been fluctuating. It also aims to assess the

strength of relationship between bark strippability and climatic variables in Chimanimani.

The results, where applicable, would help the forestry stakeholders to choose the most

appropriate silvicultural methods in trying to combat the effects of climate change. It can

be achieved by devising new strategies that optimize productivity in the present

circumstances surrounding the forestry industry. Such as shifting of the planting season

in relation to new rainfall trends, using better nursery techniques for better seedlings or

genetically improved seeds and make tactical and strategic plans in trying to improve

yields.

1.9 Assumptions

The information provided from the Planning department was valid and adequate

to make reasonable inferences.

All other factors were constant and not limiting to the maximum productivity of

the bark.

10

1.10 Scope (delimitation) of the study

The research will be focusing on identifying the influence of climate variability on

Acacia mearnsii bark strippability in Chimanimani Wattle Mimosa plantations. The

research will use 1999 – 2012 mean seasonal climatic variable data obtained from the

Wattle Mimosa planning department. The climatic elements consider were the mean

rainfall, temperatures and rain days for the harvesting season (November – March). The

other parameter considered was the bark strippability for each harvesting season

1.11 Definition of terms

Climate change – any change in weather patterns over a period of at least 30years.

Climate variability – any fluctuation in climatic elements over a short period (less than 30

years)

Climatic variables – these are elements that constitute of precipitation, temperatures and

rain days

Stripping season – the period during which harvesting of bark is done (November –

March).

Strippability – the ease to separate the bark and the stem and this is in form of a

percentage score issued by a stripper during a pre-stripping inventory.

1.12 Project layout

Chapter two discusses an overview of documented researches on the influence of climate

variability of bark strippability and yields. Chapter three presents the methodology of

analyzing the data. Chapter four consists of results while chapter five gives detailed

11

analysis of the results. Chapter six summaries the research findings and gives the

recommendations from the study.

12

CHAPTER II

LITERATURE REVIEW

2.1 Introduction

This chapter discusses some documented researches on the impacts of climate variability

on climatic variables (rainfall, temperature and rain days). It also discusses other studies

on the influence of these effects on forests and productivity and they affect strippability.

2.2 Documented researches

There is paucity of literature on the impacts of climate variability on forest productivity,

particularly with reference to specific timber products. A lot of the studies done relating

to climate have greatly focused on agricultural crops with few researches being done in

forestry.

Climate change refers to any change in climate over time (at least 30 years) whether due

to natural variability or as a result of human activity (IPCC, 2007). The variation in

climate on a shorter period is influenced by these activities and is referred to as climate

variability (IPCC, 1996). Temperature and rainfall are the major drivers of a number of

biological processes such as photosynthesis, respiration and decomposition of organic

matter. Therefore such factors can be influencing bark strippability and productivity.

According to IPCC (1996), observations have conclusively demonstrated that the

atmospheric abundance of greenhouse gases has risen dramatically since the onset of the

Industrial Age 1750. The increase was as a result of increased human activities including

industrial events, power generation, agriculture, waste emissions, deforestation, veld fires

and urbanization. These activities released exorbitant levels of greenhouse gases (CO2,

13

CH4, O3 and NOx) emissions which result in global warming and climate change (IPCC

1996). The natural greenhouse effect warms the surface of the planet to temperatures that

are hospitable for life.

Hulme et al. (2000) agrees to the fact that Africa is now warmer than it was 100 years

ago; with warming through the twentieth century having been at the rate of about 0.5°C

per century. Warming up on the land is recorded to have been higher than in the oceans

(IPCC 2001). NRC (2001) report noted that the changes observed over the last several

decades are likely mostly due to human activities, but it cannot be ruled out that some

significant parts of these changes is also a true reflection of natural variability.

2.3 The felt impacts of climate variability

Zimbabwe lies in a semi-arid region with limited and unreliable rainfall patterns and

temperature variations (Unganai, 1996). Rainfall exhibits considerable spatial and

temporal variability characterised by shifts in the onset of rains, increases in the

frequency and intensity of heavy rainfall events. Increase in the proportion of low rainfall

years as well as increase in the frequency and intensity of mid-season dry-spells have also

been noted (Unganai, 2009). Decreases in low intensity rainfall events have also been

recorded. Extreme weather events according to Mutasa (2008) have been noted to have

increased in intensity and frequency. These include tropical cyclones.

According to the Zimbabwe Meteorological Service, daily minimum temperatures have

risen by approximately 2.6°C over the last century while daily maximum temperatures

have risen by 2°C during the same period (Brown et al., 2012). Rising temperatures and

increasing rainfall variability, notably drought, are also expected to worsen declining

14

timber outputs, further compromising economic growth and stability, employment levels,

demand for other goods, and poverty reduction.

In particular, climate variation is expected to lead to the expansion of marginal lands

(IPCC, 2007), which is already beginning to occur in Zimbabwe. If changing climatic

conditions continue to expand in these regions, traditional forest systems will become

increasingly unsustainable. Tadross et al. (2009) alludes the fact that climate variability

poses a major threat to natural processes in semi- arid areas as well for moisture

depended processes in plants and bark strippability in tannin production industry.

Forestry and crop production in the absence of appropriate management practices are at

risk of frequent failure with predicted future rainfall expected to be reduced or punctuated

by concentrated heavy events separated by prolonged dry spells (Brown et al., 2012). All

these changes in the climatic variables are feared to be negatively impacting on the black

wattle in Chimanimani.

2.4 Climate variability and forest ecosystems

It has been world widely agreed upon that climate change has strongly impacted on

forestry and agriculture (Gunduz et al., 2011). Climate is a vital component that has a

critical role in the operating of forest ecosystems as it influence the status of biological,

physical and chemical processes active in the ecosystems (Sedjo and Solomon, 1989). As

a result of the close link between climate and forest health, it is assumed that noted

changes in climatic variables could be influencing strippability.

The frequency and intensity of extreme events such as droughts and the El Nino effect

have been observed to be on the increase in the recent decades in many parts of Africa

(IPCC, 2001). The swings of the ENSO cycle typically occur on a time scale of a few

15

years and disrupt vegetation through drought, heat stress, spread of parasites and disease,

and more frequent fire (Diaz and Markgraf, 1992).

Apart from climatic variations, other stresses which also increase the vulnerability of the

terrestrial ecosystems and productivity are veld or forest fires, pests, diseases and

surpassing the allowable cut (IPCC, 2007). Terrestrial ecosystems are highly dependent

on climatic factors for their growth, productivity and survival. So increase in temperature,

fire frequency, erratic rainfall patterns and the species migration as a result of climate

change has led to reduced productivity in timber and bark plantations (Bazzaz, 1990).

FAO 2003, reports that a recorded global warming averaging 0.6˚C since 1900 has

already produced devastating results in the death of trees in boreal forests and causing

major shifts in the geographic distribution of the vegetation. Fluctuations on climatic

variables such as temperature, precipitation, humidity make foresters conduct their forest

activities under the risky environment thus resulting in losses of income. (Gunduz et al.,

2011)

Climate change alters the spatial and temporal patterns of temperature and precipitation.

These are the most fundamental factors in determining distribution, productivity of

vegetation and geographical shifts in the ranges of individual tree species (Bazzaz, 1990).

Jensen (2000) concluded that a slight change in the climatic variables negatively affects

the forest structure and health. A study carried by Chenje et al in 1998 relates changes in

climate to quite a number of shifts in the drivers of the environment. These changes

include the frequency of droughts which has increased since 1990 (90/91, 91/92, 92/93,

16

93/94, 94/95, 97/98, 01/02, 02/03, 04/05 and /07) and this has caused massive drops in

crop yields in Zimbabwe. (Zhakata, 2010) This negatively impacts on the capability of

the remaining forests and woodlands to sequester carbon dioxide and also provide the

needed ecosystem goods and services (Kwesha, 1997).

2.5 Influence of climate variation impacts on forest productivity

The warming trend observed in southern Africa over the last few decades is consistent

with the global trend of temperature rise in the 1970s, 1980s and particularly in the

1990s. According to Roper (2001), it is predicted that atmospheric temperatures will rise

while rainfalls becomes erratic. Changes in the availability of water and the doubling of

carbon dioxide would cause about third of the forests worldwide to experience changes in

species composition, phenology and extent (Gunduz et al., 2011). Such predictions

therefore assumes that bark strippability will further decrease thus reducing yields.

The available soil moisture can be reduced as a result of increased evaporation rates from

high temperatures and reduced precipitation. Shifts in optimum tree growing areas and

growing seasons could affect or reduce forest productivity (Bazzaz, 1990).

Government of Namibia, (2002) reports that it has experienced warming at a rate of

0.023° C per year. Indian Ocean has also warmed with more than 1°C since 1950, a

period that has also witnessed a downward trend in rainfall (NCAR, 2005). Below-

normal rainfall years are becoming more and more frequent and the departure of these

years from the long-term normal is becoming more severe (USAID, 1992). Such changes

at that rate could not be doubted that they have negatively affected yields in forests and

these might as well had a role to play in reduction in bark strippability.

17

Numerous studies from many different ecosystems (Myers et al., 1996, Hingston and

Galbraith, 1998; Almeida et al., 2004) have demonstrated that total precipitation and its

distribution during the year have a strong influence on exotic plantation growth. And

Chimanimani black wattle are such an example. These and other studies have shown

water to be arguably the factor most limiting forest production, though site factors and

management will modify the relationship between rainfall and wood production.

Therefore a study on climatic variables becomes of necessity in the area as they also tend

to influence bark yields through reduced strippability.

Maintenance of high rates in tree growth in the main production areas of Southern Africa

may be hindered by the erratic nature of rainfall. A.mearnsii trees for example are well

adapted to short periods of water stress punctuated by rainfall events, but are vulnerable

to prolonged periods of water stress (White et al., 2000). Water stress influences the

development of A.mearnsii plantations in a number of ways. Battaglia et al., (1997) noted

that low to moderate levels of soil moisture impedes the development of leaf area and

reduces stomatal conductance. It also change patterns of biomass allocation (White et al.

1998) leading in the end to reduced stem wood production (Mendham et al., 2007). It has

been noted that these effects may continue for a period of time after water stress is

removed (White et al. 1999) and this has generally lead to reduced strippability and bark

yields. Xylem embolism occurs as the soil further dries, reducing the capacity for water

transport to leaves and inner cambium moisture thus reducing strippability. Under severe

levels of water stress leaf shedding may be induced, acclimatization of bark to wood and

finally drought death may occur (Dutkowski, 1995; Mendham et al., 2007).

18

The relationship between rainfall and growth varies from site to site but time averaged

vapour pressure deficit is a key determinant of this relationship. However, according to

Conway (2009), in the next century, northern and southern Africa is projected to become

drier with precipitation falling by 10% or more. Moderate drying of 5 to 15% per century

has already been noted along the Mediterranean coast and over large parts of Botswana

and Zimbabwe and the Transvaal in southeast South Africa (Hulme et al., 2000). Thus,

the A.mearnsii habitat in South Tropical Africa particularly in South Africa and

Zimbabwe will be affected, leading to possible population decline or even extinction.

In Zimbabwe, understanding the potential effects of changes in climate on the forest

ecosystems is critical. This is because they play an important role in the global carbon

sequestration of large amounts of CO2 from the atmosphere hence reducing the impacts

of global warming. Changes in meteorological elements (temperatures and rainfall) due

to climate change further acts as a catalyst to the changes in forests and drop in yields.

Forests also provide quite a number of goods and services (timber, wild fruits, and habitat

for animals, aesthetic value and construction poles) and they also provide a livelihood for

the rural communities. It is therefore important to understand forest dynamics through

time and space and the factors driving these changes.

Current rapidly changing climate and other associated impacts can severely impact on

the growing forests (Bazzaz, 1990). The extent to which forests and plantations are being

affected and responding to climate variability is not well understood hence the need to

study the possible impacts of global warming and climate change on the Zimbabwean

forests and how they influence productivity.

19

More than a third of the earth’s surface area is forests, accounting for an average of 85%

of plants and 35% of soil carbon (Melillo et al., 1990). Zimbabwe has an estimated total

area of 39.6 million hectares with forests occupying approximately 950 400 hectares

(Clarke and Katerere, 2003). Of this total area, 110 000 hectares are exotic plantations

and Chimanimani black wattle plantations are such an example (Shumba, 2006).

Therefore there is great need to conserve the resources and devise mitigation measures as

the changes in climate might result not only in reduced yields but also in loss of forest

areas due to increase in fire incidences and rapid shift in vegetation structures (Magadza,

1992).

Earlier greening of vegetation in spring is linked to the longer thermal growing season

due to warming. This is because forest ecosystems are highly dependent on climatic

factors (temperature and precipitation) for growth, productivity and survival. Biome

sensitivity assessments in Africa show that deciduous and semi deciduous forests may be

very sensitive to small decreases in precipitation as compared to grasslands (Jensen,

2000).

The projected rapid rise in temperature combined with other stresses such as destruction

of habitats from changes in land-use, pollution and overexploitation of resources would

affect forest structure. Other factors such as fires, pests and diseases and extreme events

can easily disrupt the interrelationships among species, transforming existing

communities and can lead to localized extinctions (Warburton and Schulze, 2006).

A study of A. mearnsii in Southern Africa, found that 47% of the standing stems which

had less than 10 cm diameter at breast height (d.b.h) were dead due to increasing aridity

in the Southern Cape region (Geldenhuys, 1981). Thin diameters at breast height are

20

usually associated with low bark strippability thus if more stems have thin diameters

(8cm>) then this would result in low yields. In the eastern and parts of central Africa,

including the Horn of Africa average rainfall is likely to increase by 15% in some parts

while decreasing by the same amount in some parts of the Sub-Savanna (Ziervogel et al.

2008; Conway, 2009). Such a scenario may or may not affect A.mearnsii survival.

Across the country, there is a shift on the onset of the rainy season from October to

November and this has disturbed the synchronous of the plantation activities. The dry

spell duration is seen to extend and prolong while the rainfall trends are seen to be

deviating from the normal as well as increase in mean seasonal temperatures thus

affecting the stripping season duration and bark strippability percentage. Therefore it can

be concluded that there is a strong need to have more researches of this nature to try and

understand the relationship on factors affecting yields and strippability.

2.6 Summary

Observations have conclusively demonstrated that Africa is now warmer than it was

100years ago by 0.5ºC (IPCC, 2007). The increase has been noted since the onset of the

Industrial Age and it has led to increase in greenhouse gases such as CO2, CH4, 03 and

NOx. Rainfall now exhibits spatial and temporal variability which is characterized by

shifts in the onset of rains, increase in frequency and intensity of heavy rains. This have

negatively impacted on the forests yields and health (Jensen, 2000) as these ecosystems

are sensitive to slight changes in climatic variables. Unganai (2009) reports that there is

increase in the ratio of low rainfall years to high rainfall and there are increases in the

frequency and intensity of mid-season dry spell. A study done by Mutasa (2008) also

noted increase in the frequency of droughts and cyclones and these directly reduce

21

productivity of the land and therefore results in reduced bark strippability. Decline in the

forests services and products has also been attributed to the increases in temperatures.

Temperatures have increased by at least 0.6ºC ± 0.2ºC (FAO, 2003). According to

Tadross et al. (2009) these increases poses a threat to plant metabolic processes thus

increasing water loss due to evapotranspiration. This had resulted to reduced soil

moisture content thus negatively influencing bark strippability. Gunduz et al. (2011)

emphasizes the fact that climate is a vital component of ecosystems processes therefore it

suggests a strong relationship between strippability and climatic variables. Several

studies suggests that precipitation received during the rainy season has a greater influence

of plantation growth (Almeida et al., 2004). A.mearnsii are adapted to short period of

water stress but vulnerable to prolonged periods of water stress (White et al., 2000). So

with the predictions of a further increase in the climatic variations, it cannot be argued

that strippability and bark yields will continue to drop if no mitigation measures are put

in place. Dutkowski (1995) also emphasizes the fact that under severe water stress

conditions, shedding of leaves maybe induced reducing photosynthesis and

acclimatization of bark to stem wood occurs lowering bark strippability. Other effects of

stress noted are thinning of diameters at breast height and if more stems per hectare have

thin diameters this would probably reduce bark yields (Geldenhuys, 1981). As a result of

these changes, they have affected the synchronous activities of the plantations thus there

is urgent need to make informed decisions based on the findings of researches that have

be done or being done.

22

CHAPTER III

METHODOLOGY

3.1 Introduction.

A total of 13 harvesting seasons were analyzed in terms of mean seasonal rainfall

patterns, mean seasonal temperatures, mean seasonal rain days and the mean seasonal

bark strippability. The available secondary data was for the study period from 1999 –

2012. The stripping season commences in November and ends in March and only means

for this period were used for the study.

3.2 Study Area

3.2.1 Location

Figure 1; Location of the study area in Zimbabwe Figure 1; Location of the study area in Zimbabwe

23

Legend

Non target area

Non target area

Compartment 37

Boundary

River

Compartment road

Minor road

Main road

Water point

The study was done in Silverstreams which is the Central Deport of Chimanimani Wattle

Mimosa. It extends from 19° 48' 0" South, 32° 52' 0" East. Silverstreams is the third

largest estate (958 ha) and it shares its boundaries (northern, southern, eastern and

western) with Tandevel, Highlands, Heathfield and Cecilton respectively.

3.2.2 Vegetation

The Eastern Highlands has an Afromontane vegetation type. The study area is under

plantation forestry which is monoculture in nature. This manipulated the area to be

infested with the invasive black wattle (A. mearnsii) trees grown for bark production. On

0 1 2

Kilometers

0

Figure 2; Location of study area in Chimanimani (Silverstreams)

24

the outskirts and boundaries of the plantations, patches of Pinus patula, P elliotti, and

Eucalyptus grandis species are found growing in association with the A. mearnsii. The

gaps or patches seen between some compartments in both estates on the map were a

result of unplanted area, infertile areas and the temporarily unplanted area after

harvesting. A.mearnsii trees are grown on 9 years rotation and only trees with diameter

greater than 8cm are harvested for bark and their wood is used for charcoal production.

Common animals species found are the Chacma baboon (Papio ursinus), kudu

(Tragelapus strepsiceros) and the common bug mired (Lygidolon laevigatum).

3.2.3 Geology and soils

Soils are derived mainly from granite and igneous rocks. They are predominantly sandy,

with heavier loamy and clay soils occurring in relatively small local areas. The soils of

the main wattle-growing areas are derived mainly from granite, quartzite, and dolerite,

with textures varying from sand to clay loam. The highest yields of wattle bark are

obtained from deep, well-drained sites irrespective of the parent rock.

3.2.4 Climate

Chimanimani falls in Zimbabwe’s agro-ecological region 1. This area is suited for

intensive farming, livestock production and horticulture. The climate varies from sub-

tropical to near temperate. It is characterized by mean annual rainfall of at least 1000mm

and mean monthly temperature of 13ºC between June to July to 25ºC in December and

January. Much of the rain falls between the month of November and March (Mugandani

et al., 2012). The rainfall pattern is generally long and reliable but of recent, it has been

seen shifting to short and erratic with prolonged dry spells (Gwaze, 1986; Mugandani et

al., 2012).

25

Figure 3; Changes in mean seasonal monthly rainfall (1999 - 2012)

3.2.5 Relief

The topography is extremely rugged, with ranges of jagged peaks and deep riverine. The

main plateau is at an altitude of 1500-1800m, with peaks reaching 2400m and dropping

to 320m in deep gorges and river valleys. The Rusitu River joins the Haroni and Bundi

rivers on the eastern side before turning to Mozambique at 312m.

26

3.3 Materials and methods

3.3.1 Experimental design

Silverstreams Central Depot was systematically selected as it was the central location

thus the influence of these variables were more of a true representative of all surrounding

estates. And also the climatic data available were taken from the central depot from 1999

– 2011 while the design below was used for the harvesting period of 2011 – 2012.

Figure 4; Experimental design used for the 2011/2012 study period

Silverstreams has always been used as the recording center for Chimanimani Estates

because of its centrality which then allows it to be used a representative of other estates.

So temperatures, rainfall and rain days were recorded at the weather station at the Central

27

depot. Strippability was calculated from the complete enumeration of all the seven

harvesting compartments. This was systematically done for the harvesting season which

extends from November to March. A total number of 4 square plots measuring 20×20m

(400m²) were randomly selected in each compartment. In that plot at least 15 trees were

randomly selected and tested for strippability by an expert stripper using an axe or

machete. A score in percentage was recorded based on the stripper’s opinion and

recorded against each tree. After that a mean strippability for each compartment was

calculated by adding all the tree strippabilities (n=60) and divide by 60. The total sample

size would be (N= 60 × 7) 540.The stripping inventory was done before harvesting of

each compartment and the compartments were distributed across the harvesting season.

3.3.2 Data collection

It has been a daily routine to record rainfall and temperature at the Wattle Company from

as early as 1998 as the records used for the study can support. Also total bark harvested

per season have been documented for production analysis back dating the same period.

3.3.3 Precipitation

Secondary data from the period 1999 – 2011 were obtained from the planning department

records while for the 2011 – 2012 data was collected from the weather station each

morning at 8am. Daily precipitation were recorded from the rain gauge at the weather

station and the mean monthly precipitation was calculated. This was done for the

stripping season (November – March) which is a 5 month period and the mean monthly

values were used for the study. The total sample of 65 months were used for analysis and

it was analysed using One way Anova from SPSS version 21.

28

3.3.4 Bark strippability

Data on strippability for the period of 1999 -2011 were obtained from the yearly returns

records in planning department. For the 2011 – 2012 harvesting season, data was

collected during the pre-stripping tests done atleast a month before the actual harvesting.

A tape measure was used to demarcate plots measuring 20m×20m (400m²) in a

compartment. At least 3 plots were randomly selected in a compartment and 15 trees

were randomly selected and tested for strippability in that plot. Each compartment had a

sample size of N=60. An expert stripper carried the stripping exercise and issued a score

for each tree. Strippability is measured as a percentage thus it was recorded in

percentages. The compartments were harvested in different months thus each

compartment was recorded against the time of harvesting. The mean strippability for that

compartment would be recorded as the mean monthly strippability. These mean monthly

strippabilities were used to see if there were any significant changes over the study

period.

3.3.5 Temperature

Secondary data was obtained from the planning department of Wattle Mimosa for the

period of 1999 to 2011. From the month of September 2011 to April 2012 daily

temperatures were recorded from the readings of the thermometers from the Stevenson’s

screen. The recording was done twice a day in the morning (8am) and in the afternoon

(2pm) and the average of the two was used as the daily mean temperature. Daily mean

temperatures were used to calculate the monthly mean temperatures and these were used

for the study. Data collected was for the harvesting season only and One Way Anova was

used to analyse the changes over the study period.

29

3.3.6 Data analysis

One Way Anova was used to analyse for changes in the climatic variables and bark

strippability while Pearson’s correlation was used to ascertain the strength of the

relationship between the climatic variables and bark strippability.

30

CHAPTER IV

RESULTS

4.1 Introduction

This chapter will show the results obtained from the data that was analysed. Results

would be were presented in the order of research objectives that is One Way Anova

results for the rainfalls, temperatures , rain days and strippability for the 1999 – 2012

harvesting / stripping season. Correlation of these variables and bark strippability were

also done and results are shown on the correlation section. SPSS version 21 was used to

analyse the data and the results were used to make conclusions and recommendations.

31

4.2 Precipitation trends

0

50

100

150

200

250

300

350

400

Rain

fall

(m

m)

Stripping season

Changes in mean seasonal rainfalls in Chimanimani

Rainfall Linear (Rainfall) 2 per. Mov. Avg. (Rainfall)

Figure 5; Graph showing mean seasonal rainfall for Chimanimani (1999 - 2012)

F12, 52 = 1.011; P > 0.05

Mean seasonal rainfall did not significantly differ (p>0.05) from 1999/00 – 2011/2012.

But there are cycles showing a decreasing trend at least on 2 years intervals.

32

4.3 Temperature trends

16.5

17

17.5

18

18.5

19

19.5

20

20.5

21

21.5

Tem

per

atu

res °C

Stripping season

Changes in mean seasonal temperatures in Chimanimani

Temperature Linear (Temperature) 2 per. Mov. Avg. (Temperature)

Figure 6; Graph showing mean seasonal temperature for Chimanimani (1999 - 2012)

F12, 52 = 4.931; P <0.001

There is a significant difference (p <0.05) in the mean seasonal temperatures from

1999/00 – 2011/2012. Temperatures have generally increased by a mean of 0.15ºC (see

appendices) over the study period. The increase is cyclic in nature on an interval of at

least 3 years.

33

4.4 Bark strippability trends.

0

20

40

60

80

100

120

Str

ipp

ab

ilit

y

Stripping season

Changes in bark strippability in Chimanimani Strippability Linear (Strippability) 2 per. Mov. Avg. (Strippability)

Figure 7; Graph showing mean seasonal bark strippability in Chimanimani

F12, 52 = 2.751; P < 0.01

Mean seasonal bark strippability shows a significant difference (P < 0.01) over the study

period. Strippability has generally decreased by at least 2% per annum over the 13

seasons. The decrease is cyclic on at least 2 years intervals. From 2006/07 the rotation

cycle has increased to at least 5 years.

34

4.5 Rain days

0

2

4

6

8

10

12

14

16

18

20

Ra

in d

ay

s

Stripping season

Changes in mean seasonal rain days in Chimanimani

Rain days Linear (Rain days) 2 per. Mov. Avg. (Rain days)

Figure 8; Graph showing mean seasonal rain days in Chimanimani

F12, 52 = 1.198; P > 0.05

There is no significant difference (P > 0.05) in the mean seasonal rain day. There are

cycles recurring on 2 -3 years intervals showing a decreasing trend.

35

4.6 Changes in the mean monthly rainfalls.

There is a significant difference in the mean monthly rainfalls from November 1999/00 to

November 2011/12 and from March 1999/00 to March 2011/12. The trend shows

oscillating fluctuations of slight increases and large decreases thus resulting in a general

reduction in the monthly rainfalls. Much of the rainfall seems to have shifted between

Nov – Dec to Feb – Mar. All the p values are < 0.05 using the t–test, thus showing a

significant difference in the monthly rainfalls over the study period.

Table 1; Mean seasonal monthly rainfall in Chimanimani Wattle Mimosa Plantations

Month November December January February March

Season

1999/00 168.5 147.5 153.5 596.5 357

2000/01 254.5 515 65.5 103 224

2001/02 146.5 40 132.5 154 428.5

2002/03 163.5 130.5 334 182.5 392.5

2003/04 72.5 191 168 108 93

2004/05 98 317 268 179 588

2005/06 73 125 434 433 89

2006/07 92 598 452 69 74

2007/08 53 270 195 318 292

2008/09 110 228 105 404 62

2009/10 187 232 38 43 108

2010/11

2011/12

98

188

168

97

119

76

76

89

62

98

Probability P < 0.05 P < 0.05 P < 0.05 P < 0.05 P < 0.05

36

4.7 Correlation between climatic variables and bark strippability.

There is a significant strong positive correlation between the rainfall and bark

strippability with an r² value of 0.715. Rain days also exhibit a positive moderate

correlation of 0.429 with bark strippability. Temperature have a negative weak

correlation of -0.277 with bark strippability over the study period shown in the table 2

below;

Table 2; Anova correlation table of climatic variable and strippability

Correlations

Rainfall Temperature Rain days Strippability

Rainfall

Pearson

Correlation

1 -.032 .712** .715**

Sig. (2-tailed) .800 .000 .000

N 65 65 65 65

Temperature

Pearson

Correlation

-.032 1 -.121 -.277*

Sig. (2-tailed) .800 .336 .025

N 65 65 65 65

Rain days

Pearson

Correlation

.712** -.121 1 .429**

Sig. (2-tailed) .000 .336 .000

N 65 65 65 65

Strippability

Pearson

Correlation

.715** -.277* .429** 1

Sig. (2-tailed) .000 .025 .000

N 65 65 65 65

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

37

4.8 The correlation between mean seasonal rainfall and bark strippability.

0

10

20

30

40

50

60

70

80

90

0

50

100

150

200

250

300

Str

ipp

ab

ilit

y (

%)

Ra

infa

ll

(mm

)

Time (season)

Correlation between rainfall and strippability

rainfall

strippability

Figure 9; Graph showing correlation between rainfall and bark strippability

R² = 0.715; P < 0.001

There is a significant strong positive correlation between the mean seasonal rainfall and

bark strippability over the study period. This is explained by the r² of 0.715. So 71.5% of

variation in bark strippability is being explained by rainfall.

38

4.8 The correlation between mean seasonal temperature and bark strippability.

17.5

18

18.5

19

19.5

20

20.5

21

0

10

20

30

40

50

60

70

80

90

Tem

per

atu

re

C

Str

ipp

ab

ilit

y (

%)

Time (season)

Correlation between temperatures and bark strippability

temperature

strippability

Figure 10; Graph showing correlation between temperature and bark strippability

R² = - 0.277; P < 0.05

There is a weak negative correlation between the mean seasonal temperatures and the

bark strippability over the 1999/00 – 2010/11 stripping season in Chimanimani.

39

4.9 Correlation between mean seasonal rain days and bark strippability.

0

10

20

30

40

50

60

70

80

90

0

2

4

6

8

10

12

14

16

18

Str

ipp

ab

ilit

y (

%)

Rain

days

Time (season)

Correlation between rain days and strippability

rain days

strippability

Figure 11; Graph showing correlation between rain days and bark strippability

R² = 0.429, P < 0.001

There is a significant moderate positive correlation between rain days and bark

strippability. The relationship is moderate as explained by R² value of 0.429 and this

assumes that 42.9% is explained by the changes in rain days.

40

CHAPTER 5

DISCUSSION

5.1 Trends in climatic variables

5.1.2 Temperature

Temperature had a significant increase from the available data of 1999 – 2012. The

increase in the temperature were in a cyclic manner at an interval of at least 3 years.

According to Tyson (1986), he also noted that temperatures were exhibiting a cyclic trend

which had an increasing trend. A mean increase of 0.15°C was recorded over the study

period of 13 years. The trend of an increase in temperatures conforms to other studies that

have been done in Zimbabwe which showed a mean increase of 0.4°C (Unganai, 2009;

Matarira and Mwamuka, 1995; Mugandani et al., 2012). Global predictions estimated a

mean global increase of 0.6 °C ± 0.2°C (IPCC, 2007). The differences in the mean

increases to my obtained value of 0.15°C and 0.4°C of other studies could be attributed to

the shorter study period of 13 years used compared to the 100 years that has been used by

other studies.

The increases in temperature are influenced by global warming (IPCC, 2007) which is a

result of increase in greenhouse gases such as CO2, CH4, 03 and NOX in the atmosphere.

Unganai (2009) also noted that the highest temperatures recorded in Zimbabwe so far

were in 2005, 2007 and 2008. This conforms to the highest peaks recorded from my

temperature graph showing synchronous and validity of my results which are in line with

what other researchers have obtained and concluded.

41

Temperature showed a negative correlation with bark strippability over the study period

of the available data. Although the link is weak (27.7%), it suggests that as temperatures

increases bark strippability decrease and vice versa. This can be seen from the graph (see

figure 10) and the weak relationship could be attributed to the short study period used.

Jensen (2000) only concludes on the sensitivity of plants to slight changes in climatic

variables particularly rainfall and temperature while Hulme et al. (2000) reported on the

shifts in vegetation structure and composition. But there is paucity of literature on the

influence of climatic variables on specific timber products including bark strippability.

Speculations and assumptions which have not be tested and proved only suggests that

there a close link thus a research of this nature was carried out although it is not being

conclusive but providing a baseline to other studies.

According to FAO (2003), it was reported that a warming trend in the global average

surface temperature of 0.6ºC since 1900 is already resulting in the death of trees in the

boreal forests and shifts in the geographic distribution of forest vegetation. So this also

led to speculations in the Chimanimani that temperature increases could be affecting bark

strippability by reducing soil moisture content resulting to acclimatization of the bark to

the stem. It then results in the decline of bark strippability as seen from 85% to 59% over

a period of 13 seasons.

Conway (2009) also reported that increase in temperatures indicate that Afromontane

ecosystems which are habitat to A.mearnsii are not going to be spared. This could be due

to increase in aridity however it is predicted that many regions of Africa will suffer from

temperature increases and droughts caused by range shifts along altitudinal and moisture

gradients (Hulme et al., 2000; Hannah et al., 2008; Conway, 2009).

42

5.1.3 Rainfall.

Rainfall did not significantly change over the study period (P>0.05). It showed a cyclic

trend which exhibited a decrease in the rainfall although not yet statistically identifiable.

Similar results were concluded by Tyson (1986) that southern African rainfall showed a

cyclic behaviour. This behaviour could be a result of linkages between rainfall and El

Nino events (Nash and Endfield, 2008) which are considered to explain partly the

occurrence of droughts. What is uncertain is whether the general perception about the

progressive desiccation in Zimbabwe is valid.

Makarau (1995) also made similar observations that rainfall had generally decreased by

10% although statistically it was not identifiable within the available rainfall time series.

Other studies (New et al., 2006, Conway et al., 2008 and Aguilar et al., 2009) concluded

that this does not imply that global warming will not cause changes of rainfall in

Zimbabwe, but the effects are not yet statistically significant within the available

historical rainfall record. Effects of climate change on annual rainfall may not be

statistically detectable due to the low signal to noise ratio arising from the high inter-

annual variability of rainfall in Zimbabwe (Makarau, 1995). The differences in the 7%

obtained from my results and the predicted 10% - 15% could be attributed to the shorter

study period that l used and the analyzing without a model as in other studies models

were used. The above mentioned researchers have attributed that rain gauge exposure and

observational procedures used may be influencing the results.

The results show increase in years of low rainfall which conforms to (Unganai, 2009)

study. It has been noted that rainfalls have shifted their onsets across the season (see

table 1) and this relates to conclusions made by Mugandani et al., 2012) in Zimbabwe.

43

Strippability directly depends on the rainfall received particularly during the stripping

season which is from November – March (UCL, 2009) thus the study only used the data

for that period as it determines the soil moisture content (Meagan et al., 1998).

There is a strong positive (71.5%) relationship between rainfall and bark strippability.

This is because as rainfall increase, the soil moisture content also rise and this increases

the moisture in the inner cambium thus bark strippability increases as well (UCL, 2009).

But if rainfall decreases, then the strippability would likely decrease as well. In 2005/06,

rainfall increased but strippability decreased and this could be as a result of decrease in

rain days. With projections of a further decrease in precipitation by IPCC, bark

strippability faces a potential threat of further decrease as rainfall continues to decrease in

Zimbabwe (Mugandani et al., 2012, Matarira and Mwamuka, 1995, Unganai, 2009).

5.1.4 Rainfall days

There is no significant difference (P>0.05) in the mean seasonal rain days over the

1999/00 – 2010/11 stripping season. From the results, a general decrease is noted in form

of 2 years cycles following the rainfall trends. This is from 1999/00 – 2001/02, 2002/03 –

2003/04, 2004/05 – 2005/06 and 2007/08 – 2010/11 while there is an increase from

2006/07 – 2007/08. The decreases in the rain days also coincides with the drought

periods (Chenje et al., 1998).

If rainfall is not evenly distributed over the rain season even when huge amounts are

received this tends to affect bark strippability (UCL, 2009). Uneven distribution of

precipitation results in prolonged dry spell duration and low soil moisture (Matarira and

Mwamuka, 1995). But if rainfall is received in small amounts but frequently distributed,

44

this leads to improved strippability concluding that both amount and distribution are of

vital role in determining the strippability.

5.1.5 Strippability

Results show a significant decrease (P<0.001) in the bark strippability from 1999/00 –

2011/12 stripping seasons. Generally there is a decrease in the bark strippability by 26%

and this is occurring in cycles which are closely linked to the rainfall patterns. As rain

days decrease, strippability is also seen to follow the same pattern resulting in a decrease

in the strippability. Bark strippability is influenced by climatic elements and these pose a

threat to productivity (UCL, 2009).

45

CHAPTER 6

CONCLUSSIONS AND RECOMMENDATIONS

6.1 Conclusions

It can be concluded that climate variability have got a negative influence on climatic

variables which include rainfall, temperature and rain days. Over the study period it can

noted that temperature have significantly increased and it has got a negative influence on

bark strippability although it showed a weak relationship. This can be attributed to the

length of the study period and available data which can either be to exhibit significant

inferences in the data and make conclusive conclusions.

Rainfall and rain days have generally decreased over the study period although rainfall

was not statistically showing the significance. It be concluded that these two have a

strong influence on bark strippability with rainfall having a stronger link compared to

rain days and to temperatures. As rainfall and rain days increase, bark strippability is seen

to increase as well while a decrease of these two or one of them have a negative impact

on bark strippability.

Strippability can be concluded that it has decreased from the analysis done of the

available data. It also shows a close link with the climatic variables thereby a need to

devise mitigation measures as predictions suggests that there would be a further increase

in these felt impacts if no mitigation measures are implemented.

46

Overall it can be concluded that climatic variables shown a variation which has got a

close link with bark strippability and from the data available, these climatic elements

have been concluded to be negatively influencing bark strippability.

6.2 Recommendations

From the results l obtained l therefore recommend that a standard way of measuring bark

strippability be improvised which is not based on the stripper’s opinion. For example a

length of one meter be tested for strippability and the length that would strip over the

total length could be expressed as a fraction in percentage.

I also recommend that a similar study be done in another area like Chipinge for

comparison purposes so that conclusions can be made based on more studies and if

possible a longer period can be considered if the secondary data is available.

It could be recommended that localized models be used to predict rainfall patterns and

temperatures for the country. This could also be able to exhibit inferences within the

available time series data which has not be statistically identifiable.

6.3 Suggestions

I also suggest that drought and temperature resistant seeds be used to improve yields as

well as more labour being channeled towards the stripping activity during the harvesting

to as to finish while rains are still available. Also seasonal or contract workers can be

employed to copy with the rains.

Breeding programs can be made available to reduce establishment costs and have

seedlings which are suitably adapted to the adverse impacts of variability and the

environment.

47

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APPENDICES Appendix 1; Research data collected

Season

Rainfall

(mm)

Temperature

°C

Rain

days

Strippability

%

1 168.5 17.8 15 92

1 147.5 18 9 87

1 153.5 19.3 16 85

1 596.5 19.1 23 89

1 357 19.1 19 76

2 254.5 18.6 11 80

2 515 18.3 26 84

2 65.5 19.5 9 77

2 103 19.7 7 75

2 224 19.9 11 76

3 146.5 18.2 9 72

3 40 18.8 6 43

3 132.5 20.1 10 68

3 154 20.3 11 77

3 428.5 20.7 12 81

4 163.5 18.3 12 69

4 130.5 18 8 67

4 334 19.4 14 77

4 182.5 19.7 13 70

4 392.5 19.4 22 81

5 72.5 18.8 7 52

5 191 18.5 16 76

5 168 19.6 12 67

5 108 19.8 10 62

5 93 20.3 12 60

6 98 19.8 5 64

6 317 19.7 20 81

6 268 20.6 15 79

6 179 20.2 11 72

6 588 20.7 20 87

7 73 20.1 9 66

56

7 125 19.9 6 65

7 434 20.8 16 82

7 433 20.5 13 82

7 89 21.2 8 62

8 92 19.7 11 64

8 598 19.5 24 88

8 452 20.1 18 84

8 69 19.9 6 64

8 74 20.3 7 66

9 53 20 7 52

9 270 20.3 18 75

9 195 20.9 16 70

9 318 21.3 15 79

9 292 21.5 13 72

10 110 19.2 11 62

10 228 18.7 10 76

10 105 19.6 8 60

10 404 19.4 15 84

10 62 20.1 9 60

11 187 19.4 8 77

11 232 18.9 9 75

11 38 19.8 25 31

11 43 20.3 9 34

11 108 20.1 3 65

12 98 19.8 7 61

12 168 19.6 5 77

12 119 20.5 17 54

12 76 21.2 5 54

12 62 20.9 2 50

13 188 20.2 9 63

13 97 19.5 6 74

13 76 20.7 13 60

13 89 20.8 7 43

13 98 21.2 4 56

Season 1 = 99 (Nov – Dec), 00 (Jan, Feb and March)

Season 2 = 00 (Nov – Dec), 01 (Jan, Feb and March)

... … … …

Season 13 = 11 (Nov – Dec), 12 (Jan, Feb and March)

57

Appendix 2; Anova table for differences in climatic variables and strippability

Appendix 3; Pearson‘s correlation table of climatic variables and strippability

Correlations

Rainf

all

Temperatu

re

Rain

days

Strippabi

lity

Rainfall Pearson

Correlati

on

1 -.032 .712**

.715**

Sig. (1-

tailed)

.400 .000 .000

N 65 65 65 65

Temperatu Pearson -.032 1 - -.277*

ANOVA

Sum of

Squares

df Mean

Square

F Sig.

Rainfall Between

Groups

261666.7

15

12 21805.56

0

1.011 .452

Within

Groups

1121130.

800

52 21560.20

8

Total 1382797.

515

64

Temperatu

re

Between

Groups

26.038 12 2.170 4.931 .000

Within

Groups

22.880 52 .440

Total 48.918 64

Rain days Between

Groups

421.446 12 35.121 1.198 .310

Within

Groups

1524.400 52 29.315

Total 1945.846 64

Strippabi

lity

Between

Groups

4236.738 12 353.062 2.751 .006

Within

Groups

6673.200 52 128.331

Total 10909.93

8

64

58

re Correlati

on

.121

Sig. (1-

tailed)

.400 .168 .013

N 65 65 65 65

Rain days Pearson

Correlati

on

.712** -.121 1 .429**

Sig. (1-

tailed)

.000 .168 .000

N 65 65 65 65

Strippabi

lity

Pearson

Correlati

on

.715** -.277* .429**

1

Sig. (1-

tailed)

.000 .013 .000

N 65 65 65 65

**. Correlation is significant at the 0.01 level (1-tailed).

*. Correlation is significant at the 0.05 level (1-tailed).

Appendix 4; Mean (SE) of climatic variables and bark strippability.

Season Rainfall Strippability Rain days Temperature

1999/00 284.6 ± 87.2 78.4 ± 2.8 16.4 ± 2.3 18.66 ±

0.31

2000/01 232.4 ± 79.1 72.0 ± 5.4 12.8 ± 3.4 19.2 ± 0.32

2001/02 180.3 ± 65.3 68.2 ± 6.7 9.6 ± 1.0 19.62 ±

0.47

2002/03 240.6 ± 51.6 72.8 ± 2.7 13.8 ± 2.3 18.96 ±

0.34

2003/04 126.5 ± 22.7 63.4 ± 4.0 11.4 ± 1.5 19.4 ± 0.33

2004/05 290.0 ±83.4 76.6 ± 4.0 14.2 ± 2.9 20.2 ± 0.20

2005/06 230.8 ± 83.2 71.4 ± 4.4 10.4 ± 1.8 20.5 ± 0.23

2006/07 257.0 ±

111.9

73.2 ± 5.3 13.2 ± 3.4 19.9 ± 0.14

2007/08 225.6 ± 47.8 69.6 ± 4.7 13.8 ± 1.9 20.8 ± 0.29

2008/09 181.8 ± 62.0 68.4 ± 4.9 10.6 ± 1.2 19.4 ± 0.23

2009/10 121.6 ± 38.6 56.4 ± 10.0 10.8 ± 3.7 19.7 ± 0.25

2010/11 104.6 ± 18.6 53.2 ± 8.6 7.20 ± 2.6 20.4 ± 0.31

Probability 0.587 ˃ 0.05 0.006 < 0.05 0.459 ˃ 0.05 0.001˂ 0.05

59

Appendix 5;