POWELL MPONELA. BSc. Forestry (Mzuzu University). Thesis submitted to the Faculty of Environmental Sciences in ...... Green jobs: towards decent work in a.
HOUSEHOLD SOCIO-ECONOMIC FACTORS AND PLANT SPECIES DIVERSITY AFFECTING ALLOCATION OF MARGINAL LANDS TO JATROPHA CURCAS L. IN MALAWI
POWELL MPONELA BSc. Forestry (Mzuzu University)
Thesis submitted to the Faculty of Environmental Sciences in partial fulfilment of the requirement for the degree of Master of Science in Social Forestry
UNIVERSITY OF MALAWI BUNDA COLLEGE OF AGRICULTURE MAY 2010
DECLARATION I declare that this dissertation constitutes original work and has not been presented for any other awards at this or any other university. All sources of information have been fully acknowledged in the references.
Powell Mponela
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CERTIFICATE OF APPROVAL We, the undersigned, certify that the material presented in this thesis, is the candidate’s original work, and that it has not been submitted for any award at any institution. This thesis is accepted in form and content. It has satisfactory knowledge of the field covered.
MAJOR SUPERVISOR: DR. WESTON F. MWASE Signature
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CO-SUPERVISORS: DR. CHARLES B.L. JUMBE Signature
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MR. MOSES D. NTHOLO Signature
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DEDICATION To my loving father Mgongo and caring mother Nyamjimira: MAY YOUR SOULS REST IN ETERNAL PEACE
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TABLE OF CONTENTS
DECLARATION .................................................................................................................... I CERTIFICATE OF APPROVAL ......................................................................................II DEDICATION ..................................................................................................................... III LIST OF TABLES ........................................................................................................... VIII LIST OF FIGURES .............................................................................................................. X LIST OF ABBREVIATIONS AND ACRONYMS ........................................................ XI AKNOWLEDGEMENTS .................................................................................................XII ABSTRACT....................................................................................................................... XIII CHAPTER ONE ..................................................................................................................... 1 1.0
INTRODUCTION .................................................................................................. 1
1.1
Background information.......................................................................................... 1
1.2
Rationale for the study............................................................................................. 4
1.3
Study objectives ....................................................................................................... 5
1.3.1
Main objectives for the study .........................................................................5
1.3.2
Specific objectives ...........................................................................................5
1.4
Hypotheses ............................................................................................................... 5
CHAPTER TWO.................................................................................................................... 6 2.0
LITERATURE REVIEW...................................................................................... 6
2.1
Jatropha curcas production on marginal and degraded land ................................ 6
2.2
Conservation of plant species diversity in Malawi ................................................ 9
2.3
Household decision to change land use ................................................................16
CHAPTER THREE .............................................................................................................19
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3.0
METHODOLOGY ...............................................................................................19
3.1
Study site ................................................................................................................19
3.2
Plant species Diversity ..........................................................................................21
3.2.1
Sampling framework, design and sample size............................................ 21
3.2.2
Data collection for species diversity ........................................................... 23
3.2.3
Measurement of plant species diversity ...................................................... 24
3.2.3.1
Species richness ............................................................................................ 25
3.2.3.2
Species diversity ........................................................................................... 26
3.2.3.3
Species evenness .......................................................................................... 29
3.2.3.4
Species abundance ........................................................................................ 29
3.2.3.5
Beta diversity ................................................................................................ 30
Determinants of Jatropha curcas adoption ..........................................................30
3.3 3.3.1
Conceptual framework ................................................................................. 30
3.3.2
Data Sources ................................................................................................. 33
3.3.4
Analytical Models ........................................................................................ 35
3.3.4.1
Logit Model .................................................................................................. 37
3.3.4.2
Tobit model ................................................................................................... 39
CHAPTER FOUR ................................................................................................................41 4.0
RESULTS...............................................................................................................41
4.1
Plant species diversity ...........................................................................................41
4.1.1
Species richness ............................................................................................ 41
4.1.2
Species abundance ........................................................................................ 42
4.1.3
Species diversity of trees and shrubs and of herbaceous plants ................ 47
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4.1.4
Floristic similarity between study site classes ............................................ 48
4.1.5
Taxonomic richness...................................................................................... 49
4.1.6
Threatened, protected and high value species ............................................ 50
4.1.7
Edaphic conditions of the study sites .......................................................... 51
4.2
Household decision to convert landuse of marginal and degraded areas to J. curcas................................................................................................................52
4.2.1
Descriptive results of household characteristics......................................... 52
4.2.2
Determinants of Jatropha curcas cultivation ............................................. 59
4.2.3
Intensity of adoption of Jatropha curcas .................................................... 61
CHAPTER FIVE ..................................................................................................................66 5.0
DISCUSSION ........................................................................................................66
5.1
Plant species diversity ...........................................................................................66
5.2
Household decision to plant Jatropha curcas ......................................................71
CHAPTER SIX .....................................................................................................................76 6.0
CONCLUSION AND POLICY IMPLICATIONS..........................................76
6.1
Plant species diversity ...........................................................................................76
6.2
Household decision to plant Jatropha curcas ......................................................76
REFERENCES .....................................................................................................................78 APPENDICES.......................................................................................................................94 Appendix 1: Plant diversity data collection form.............................................................94 Appendix 2. A questionnaire for household heads ..........................................................96 Appendix 3. Heckman estimates of household decision to allocate land to J. curcas in Mzimba ..............................................................................................102
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Appendix 4: Correlation Matrix between independent variables of logistic models for Mzimba (upper layer) and Kasungu (lower layer) .....................................103 Appendix 5: Minimum and maximum temperatures and rainfall for Mzimba and Kasungu districts for the period 1990 to 2009. ................................................104
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LIST OF TABLES Table 1: Site classification using district, farming system, and cultivation status ..........23 Table 2: Specification of the predictor variables used in binary logistic and Tobit models and direction of influence (signs) .............................................................38 Table 3: Richness of trees, shrubs and herbaceous plants (per hectare) within different farm systems and cultivation status ........................................................41 Table 4: Species diversity and evenness within different farm systems and cultivation status .....................................................................................................47 Table 5: Percent similarity of tree and shrub and of herbaceous species among the six site classes .........................................................................................................48 Table 6: Genera of woody plants with more than two species .........................................50 Table 7: Soil chemical characteristics between six site classes (mean±S.E.) .................51 Table 8: Demographic characteristics of household heads by household type in Mzimba and Kasungu districts (%) .......................................................................52 Table 9: Proportion of household head age groups and overall mean of household heads ........................................................................................................................54 Table 10: Percent household heads that have attained formal education and mean education level by district and household group...................................................54 Table 11: Mean values of quantitative predictors among households that plant Jatropha curcas (JP) and those that does not (NJP) in Mzimba and Kasungu. ..................................................................................................................56 Table 12: Ownership of fallow and uncultivated land (percentage of respondents) in Mzimba and Kasungu .........................................................................................58
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Table 13: Percent composition of households by age and farmer group in Mzimba and Kasungu ............................................................................................................59 Table 14: Socioeconomic factors influencing household’s decision to plant J. curcas.......................................................................................................................60 Table 15 Tobit estimates of household’s decision to assign land to J. curcas in Mzimba ....................................................................................................................63 Table 16: Partial derivatives and elasticities of land cultivated with J. curcas ...............64
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LIST OF FIGURES Figure 1: Map of Malawi showing study sites for Jatropha curcas ................................19 Figure 2: K-Dominance showing the diversity of woody plants within the six site classes ......................................................................................................................42 Figure 3: K-dominance plot showing the diversity of herbaceous plants........................43 Figure 4: Species abundance (number of plants times 50 ha) plots between cultivated 1MSC and uncultivated 2MSU in Mzimba (a); cultivated 3KSC and uncultivated 4KSU subsistence landscapes in Kasungu(b); cultivated 5KCC and uncultivated 6KCU commercial landscapes in Kasungu (c) and between subsistence and commercial farms in Kasungu that were cultivated (d) and uncultivated (e). ........................................................................46
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LIST OF ABBREVIATIONS AND ACRONYMS AEDO
Agricultural Extension and Development Officer
BERL
Bio Energy Resources Limited
CA
Conserve Africa
CBD
Convention on Biological Diversity
DCA
Detrended Correspondence Analysis
EPA
Extension Planning Area
FAO
Food and Agricultural Organisation
FRA
Forest Resources Assessment
FRIM
Forestry Research Institute of Malawi
GIS
Geographic Information System
GoM
Government of Malawi
GPS
Global Positioning System
IUCN
International Union for Conservation of Nature
IUFRO
International Union of Forest Research Organizations
LU
Livestock Units
MoA
Ministry of Agriculture
NGOs
Non Governmental Organisations
NSDC
National Spatial Data Centre
NSO
National Statistical Office
RBA
Rapid Biodiversity Assessment
SPSS
Statistical Package for Social Scientists
UCLA
University of California, Los Angeles
UNEP
United Nations Environmental Programme
VES
Visual Encounter Survey
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AKNOWLEDGEMENTS I would like to thank my supervisors Dr. W.F. Mwase, Dr. C.B.L. Jumbe and Mr. M.D. Ntholo for their expert guidance and critique from early stages of proposal formulation up to thesis production. Special gratitude goes to Mr. Frank Babka for providing me with financial support for tuition, accommodation, laptop and research funds that enabled me to carry out this study. I am grateful to farmers and landowners who responded to questionnaires and allowed us to visit their farms and take plant and soil samples. I am also indebted to Bio Energy Resources Limited (BERL) for granting me access and directing me to contracted J. curcas growers. I am thankful to the data collection team comprising of Tracy Mponela, Senk Kalowekamo, Agness Mkhwewu, Boyd Zulu and Mr. Dawa for working diligently from early morning till late evening. Finally, I would like to thank my brothers and sisters, masters students residing in Social Forestry hostel and Silimyake Mwenechanya for social and moral support.
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ABSTRACT Malawi is experiencing an influx of bio-fuels projects that target farmers with unproductive land. This study examines plant species diversity on marginal and degraded land earmarked for Jatropha curcas and determinants of its adoption. A rapid biodiversity assessment using visual encounter survey of plant species was conducted between patches of degraded fallow land and uncultivated marginal areas within commercial and subsistence agricultural landscapes. Measures of species richness and abundance were used to assess the effect of intensified cultivation on biodiversity. Socioeconomic data from household survey were analysed to determine household adoption decision and extent of land conversion to J. curcas. Results indicate that cultivation and intensified farming significantly influenced floristic composition within agricultural landscapes. Fallow areas were floristically rich in herbaceous plants that are adapted to frequently disturbed sites but poor in trees as opposed to uncultivated areas. Uncultivated areas reserved some endangered species including high value Pterocarpus angolensis. On the determinants of adoption, results show that age and education of household head and availability of labour positively influenced the decision to plant J. curcas where as ownership of livestock and non-farm income deterred households from cultivating the crop. This suggests that poor households are more likely to adopt J. curcas than richer households with livestock and income from non-farm activities. Ownership of uncultivated land influenced J. curcas cultivation in Mzimba. In Kasungu households with fallow land were more likely to plant J. curcas. From the analysis, uncultivated areas within agricultural landscapes have higher potential for conservation of indigenous woody species. These need to be spared as village conservation sites. In other words, Jatropha curcas cultivation should concentrate on degraded fallow areas as they contain species that are adapted to frequent disturbance. In Mzimba, cautionary measures have to be put in place to ensure that farmers do not plant J. curcas in uncultivated woody areas that have rich biodiversity. To enhance adoption, entrepreneurship programmes need to be designed to provide income to farmers during the gestation period of the crop. Being a new crop, there is need for educational programmes on crop management and on-farm research of J. curcas to increase the understanding of this novel crop and scale-up its adoption.
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CHAPTER ONE 1.0 INTRODUCTION 1.1
Background information
The global concerns on energy security, food shortages, climate change and increasing oil prices are rising and countries around the world have set their sights on the development of alternative sources of energy (Hung, 2009). Jatropha curcas bio-diesel is recognized around the world as a renewable biomass energy that has potential to replace petroleumbased diesel (Renner et al., 2008). Successful test flights by Virgin Atlantic, Air New Zealand, Continental airlines and Japan airlines with a blend of J. curcas fuel provides impetus for its use as a sustainable second-generation aviation fuel (Air New Zealand, 2008; Young, 2009). In Malawi, J. curcas fuel is intended to be blended with diesel for domestic use in vehicles and ships such as Chauncy Maples and the surplus will be exported (Chauncy Maples, 2009).
Benge (2006) cited availability of suitable land for growing Jatropha curcas without detrimental effects on food production and biodiversity as one of the major challenges in promoting this crop. Parsons (2005) indicated that equatorial regions located between 25°N and 25°S latitude have optimal conditions for growing J. curcas. In Africa, about 1080 million hectares are considered prime growing areas for the crop. In Malawi, most projects use small-scale, community-based initiatives by contracting both subsistence and commercial farmers to grow J. curcas on unproductive land which are unsuitable for farming. The implementation of this approach needs extension efforts through local networks having good insight in local environment, economic, cultural and social
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processes. The most important condition for the success of such a pathway is that this small-scale model benefits the adopting farmer (Achten et al., 2009).
Despite the promotion of J. curcas cultivation by smallholder farmers, apparently farmers in Malawi are constrained by land shortage. Nearly 55% of smallholder farmers have less than 1 hectare (ha) of cultivable land which is not enough to produce crops to meet their daily food needs (GoM, 2002). Based on available resources and land productivity, people have traditionally devised selective resource use systems that maintain a diverse resource base within the homesteads and agricultural fields. Marginal areas have been reserved as woodlands and provide a useful source of various indigenous plant products and services. Farmers also put pieces of degraded land to natural or improved fallow in order to organically regain agricultural productivity. They derive fuelwood for cooking and heating such that 94% of Malawi’s population depend on biomass energy (Kainja, 2000). The rural dwellers also depend on grass and indigenous trees for housing and construction. About 84% of the country’s population live in grass thatched houses whose roofs and walls are reinforced by woody poles (NSO, 2005). Smallholder livestock farmers depend on natural vegetation estimated at 2.7 million ha for fodder and pasture (Reynolds, 2006). Medicinal plants are a major source of traditional medicine (Conserve Africa, 2004). For many generations, various plants and plant parts have been, used to cure different ailments. People also use a variety of natural plants to supplement their meagre diets; a variety of herbs are used as relish and indigenous fruits provide a safety net during lean months (Babu, 2000; Hag et al., 2008). Various species of grasses, sedges, herbs, trees and ferns are used by households during different seasons of the year.
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The present plethora of bioenergy projects prioritising marginal and degraded areas as a strategy to enhance rural incomes while avoiding competition with food production does not go unnoticed (Del Greco and Rademakers, 2005). Benge (2006) points out that closely spaced J. curcas over large areas would result in higher yields but also would lead to unplanned elimination of grasses and shrubs thereby threatening biodiversity. With the CBD-COP 9 decision (CBD, 2008), it is internationally agreed that biofuels production and use should be sustainable in relation to biological diversity. The parties also stressed that risk mitigation measures are needed to minimize the negative impacts of biofuels production and its use on biodiversity and the livelihoods of local communities. Meena and Sharma (2006) noted that J. curcas is a novel crop and has received limited research. As the production of J. curcas increases, concerns arise about the impact of its adoption on genetic diversity, farm size and input use in agriculture and a variety of social issues. To date no studies have been conducted in Malawi to assess the current status of plant resources and determinants of household’s decision to plant J. curcas. This study is therefore conducted to address the following questions: What is the current status of plant species diversity in marginal and degraded areas? What biophysical conditions and anthropogenic factors underlie the variation in plant species diversity within marginal areas? What factors determine household’s decision to change land use by planting J. curcas on these unproductive areas? Answers to these questions are vital for integration of ecological values and social dimension into economically driven biofuels programmes. Again, results of this study provides policy prescriptions for up-scaling J. curcas cultivation without disturbing the ecosystem and factors that need to be considered to influence farmers’ decision to adopt J. curcas in their farming system.
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1.2
Rationale for the study
The information on plant species diversity is critical in ensuring that J. curcas cultivation does not lead to destruction of local biodiversity. Biophysical and anthropogenic factors induce changes in diversity and can help explain the behaviour of ecosystems (Heywood and Baste, 1995). Information on determinants of adoption of J. curcas is essential for prescribing appropriate policies designed to scale-up production of J. curcas. Human induced loss of species diversity especially resulting from deforestation, agricultural extensification and mismanagement of resources is of increasing concern (Matson et al., 1997; Krebs et al., 1999; Clough et al., 2007). Conservation of plant species within patches of marginal and degraded areas has been considered to be a step towards reversing this trend (Hutson, 1993; Bai and Dent, 2006)). Furthermore, these areas are excellent reserves for conservation of agro-biodiversity which is vital for maintaining a stable agro-ecosystem (UNEP, 1999). The government, non-governmental organisations and the private sector seek to intervene via policy instruments and programmes. Marginal areas are also targeted as potential sites for growing bioenergy crops as a way of reducing competition with food crops (Del Greco and Rademakers, 2005). Jatropha curcas cultivation is being promoted as one of the best options to improve land productivity and provide cash incomes to the rural dwellers. However, Benge (2006) observed that J. curcas grown on unproductive land is likely to give marginal yields and to be economical as a biodiesel fuel it must be grown over extensive areas. Therefore, the success of J. curcas programs depends to a large extent on the manner by which households respond to policy interventions (McGregor et al., 2001).
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1.3
Study objectives
1.3.1
Main objectives for the study
The primary objective of the study is to assess factors affecting plant species diversity and household’s decision to integrate J. curcas into their farming system on marginal areas.
1.3.2
Specific objectives
1. To evaluate the influence of cultivation and agricultural intensification on plant species diversity and floristic composition. 2. Identify threatened or endangered plant species within areas earmarked for J. curcas cultivation for conservation. 3. To examine socioeconomic factors influencing household’s decision to assign unproductive land to J. curcas. 1.4
Hypotheses
1. Anthropogenic disturbances have not led to changes in plant diversity by narrowing the number, composition and abundance of plant species. 2. Socioeconomic factors such as household age, household livestock and/or ownership of uncultivated land do not influence farmer’s decision to grow Jatropha curcas on marginal and degraded areas.
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CHAPTER TWO 2.0 LITERATURE REVIEW This chapter reviews the literature on production of Jatropha curcas on marginal and degraded areas, conservation of plant species diversity and household decision to plant J. curcas on unproductive land.
2.1
Jatropha curcas production on marginal and degraded land
Jatropha curcas L. (Physic nut) is a deciduous shrub that grows up to 3-5 m, with a productive life of 50 years and belongs to the Euphorbiaceae family. It originated from Central America and was distributed by Portuguese seafarers (Makkar et al., 1997). It is an interesting but underutilized crop, now being increasingly promoted in reforestation programs in tropical countries for biofuels production because it thrives on poor soils and on land that is suffering under erosion (Henning, 2008). Jatropha curcas is a multipurpose shrub and is considered of great potential to solve multiple problems faced by the rural poor (Sugrue, 2008). It has been planted as hedges around gardens and fields, protecting crops from browsing animals, controlling soil erosion and demarcating boundaries of fields and homesteads. The oil from the seeds is used for producing soap, household energy, glycerine and the residues made into nitrogenous organic fertilizer. The plant is also used as a medicinal plant: seeds against constipation; sap for wound healing; leaves as treatment against malaria (Sugrue, 2008).
As a commercial crop, J. curcas is being promoted globally for biofuels production. Most countries have been using food crops including corn in the United States of America, wheat and rapeseed in the European Union and sugar cane in Brazil to produce 6
biofuels. Sourcing raw materials for the biofuels industry has become a problem due to the food scarcity resulting from people, livestock and biofuels competition for food supplies. According to the United Nations’ Food and Agriculture Organization (FAO), the use of grains, sugar and other crop seeds and vegetable oils for production into biofuels has resulted in shortage of food and steady rises in food prices (Yang, 2007). J. curcas is considered a candidate biofuels crop because it grows on unproductive areas hence reduces competition with food crops. Moreover, the oil content of J. curcas fruit ranges from 30% to 61% which is higher than other commonly used oil bearing crops such as rapeseed and soybean (Henning, 2008).
Data on production levels have been obtained from fertile soils but also from young plantations of 1-2 years old. The yields ranges from 0.6 to 4.1 t seed ha-1 (Jongschaap et al., 2007). Jatropha curcas stands can reach maturity and full production within 3-4 years after planting and the projected yields from mature stands may go as high as 8 t seed ha-1 (Parsons, 2005; Jongschaap et al., 2007). Data on production levels in marginal and degraded lands are not readily available but it has been argued that production on marginal land can contribute to the rural development without competing with food production and biodiversity (Jongschaap et al., 2007).
Marginal land refers to an area where a cost-effective production is not possible, under given site conditions (e.g., soil productivity), cultivation techniques, agriculture policies as well as macro-economic and legal conditions (Schroers, 2006). Degraded land is characterised by a long-term decline in ecosystem function and productivity and measured in terms of net primary productivity (Wiegmann et al., 2008). Marginal and
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degraded areas are, in terms of agricultural production, unproductive but cover 40% of the total land area of Malawi (Reynolds, 2006). Studies by Leonard (1989), Fan and Hazell (1997) and World Bank (2005) in developing countries showed that most poor people are shifted to marginal areas. In Malawi during colonial era, most poor and marginalised groups were shifted to marginal areas where their livelihoods have depended on them. They clear woodlands and grasslands without conservation concerns leading to land degradation. Degraded areas are left as a fallow to naturally regain vegetation. Farmers rely on the natural regrowth for several products and ecological services. Due to shortage of productive land, farmers clear even ecologically fragile land. World Bank (2005) pointed out that agricultural systems in these areas provide farmers with precarious existence, hence, they are prioritised for biodiversity conservation (Bai and Dent, 2006). Furthermore, these areas are excellent reserves for conservation of agrobiodiversity in agricultural landscapes (UNEP, 1999). Recent developments in bio-energy have also prioritised marginal areas for cultivation of energy crops such as Jatropha curcas to avoid competing with food crop production.
In terms of research, Hines (1998) noted that marginal, low-potential areas tend to be remote and isolated, prone to environmental shocks, and often bypassed by research and development programmes. The bypassing of these areas is partly due to an insufficient understanding of the relationship between poor people’s food insecurity and their dependence on marginal low-potential ecosystems. It also stems from the need for foodinsecure people to obtain short-term benefits, whereas people concerned with protecting the environment tend to have a long-term focus (Davies et al., 1991). Therefore, the greatest challenge is how best to balance the polar goals of agricultural production and
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environmental stewardship (Ajayi et al., 2007). Achieving a balance between natural resource use and food security requires guaranteeing food availability and access for the poorest today while managing production systems for the health of the natural resource base tomorrow.
In Malawi, despite the over-reliance on agriculture and natural
resources, only 40% of the total land area is considered productive (Reynolds, 2006). Due to population increase, land is the major limiting factor of production in agriculture. Sustainable agricultural intensification is virtually impossible in the face of poverty where 52.4% of the 12.6 million Malawians live below the poverty line and 22.3% below ultra poverty line (NSO, 2005). In the quest to reconcile the food security deficit of today with the environmental debt of tomorrow, there is a tendency to prioritise agricultural production over the concern for the environment. Adjustment and adaptation towards increasing population density was initially made possible through extensification (Shiferaw, 2006). However, as opportunities for expansion disappear, farmers have encroached into marginal areas which are unsuitable for farming without the necessary resource-improving investments. This has led to deforestation, land degradation, reduced biodiversity and rural dwellers potential to adapt to adverse climatic conditions (Mwase et al., 2007; Temu and Kiwia, 2008).
2.2
Conservation of plant species diversity in Malawi
Malawi is endowed with a vast variety of plant species. In terms of woody species, Gowela and Masamba (2002) observed that there are two most common vegetation types in Malawi. These are the miombo woodlands dominated by Brachystegia, Julbernardia and Isoberlinia species, and the Acacia-Piliostigma-Combretum wooded savannah. Both
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occur mainly in the medium-altitude plain. The high-altitude high-rainfall plateaus with sodic soils are dominated by montane grassland vegetation. The main woody species in these grasslands is Colospermum mopane. The Lakeshore plain and the lower Shire Valley are characterized by a savannah bush-grassland and a thicket with Acacia spp., Sterculia spp., Cordyla africana and scattered Adansonia digitata and Hyphaene ventricosa.
Makungwa and Kayambazinthu (1999) studied woody species diversity in Chimaliro and Liwonde forest reserves and found that dominant species were Uapaca kirkiana, Brachystegia speciformis, B. bussei, and B. utilis. Detrendend correspondence analysis (DCA) showed that environmental gradient had no significant influence on species diversity. A total of 112 species were recorded.
FAO through a global Forest Resource Assessment (FRA) estimated Malawi’s forest cover in 2005 at 3.402 million hectares representing 36% of total land area. The estimated annual loss of forests between 2000 and 2005 was 33,000 ha at a rate of 0.93%. Due to intensive use to which the woodlands have been subjected, it is widely accepted that there is very little unmodified woodland remaining. In Malawi, over 95 percent of existing woodland cover has been heavily modified (FAO, 2005). With increasing population and other pressures, the area under forests is on the decline which leads to the reduction of biological diversity. Given that accelerating rates of tropical deforestation are threatening these ecosystems, FAO (2006) recognised the importance of recognizing biodiversity hotspots in order to develop effective management and conservation policies.
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According to the International Union for Conservation of Nature (IUCN), in the year 2000, several species entered into red list as endangered or vulnerable species (FAO, 2005). Three tree species were listed as endangered and 10 as vulnerable. A review by Msekandiana and Mlangeni (2002) in Southern Africa listed 133 plant species as extinct or threatened, 51 species as lower risk and 63 species as data deficient. Most of these species are endemic and found at higher altitudes on Mulanje Mountain and Nyika Plateau. As a way of conserving forest genetic resources, the Government of Malawi declared some species on customary land as protected species (GoM, 1997). Malawi has also prioritized six indigenous fine hardwoods that yield high-grade timber and six less commonly used fine hardwoods for conservation due to demand on their products (Gowela and Masamba, 2002). Herbalists in Malawi primarily collect medicinal plants from natural forests or woodlands. Habitats in the vicinity of many herbalists are subject to more collecting pressure threatening the survival of medicinal plants (Gowela and Masamba, 2002). Eight commonly used medicinal plants have been identified as endangered.
For many years, the Government of Malawi protected forest resources and catchment areas. Sixty six forests were gazetted as protected forest reserves (Gowela and Masamba 2002). The government deployed forest guards to protect the ecosystems such as forest reserves, national parks, catchment conservation areas and game reserves from encroachment by the local communities. Local people living close to these areas, whose livelihoods were dependent on them, were denied access to the resources. This culminated into conflicts and governments failure to efficiently manage the resources.
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In 1996, the government developed forest policy which allowed the local people to participate in the co-management of the protected areas (GoM, 1996; GoM, 1997; GoM, 1998; GoM, 2001). The government and nongovernmental organisations (NGOs) facilitated creation of community organisations and formed village committees to manage natural resources on customary land. Communities have also been granted usufruct rights through co-management arrangements to take part in management of protected areas close to them and on which their livelihoods depend.
There is an intricate link between humans and distribution and abundance of plant species. Humans depend on plant resources for food, energy, construction materials, and medicine. Further, plants resources have critical character of being renewable, so with proper management they can be used sustainably. However, when the levels of human use of plant resources exceed their capacity for regrowth, the diversity and productivity of the system in which they occur may be reduced (McNeely et al., 1995). As noted by Dallmeier and Comiskey (1998) human activity transforms diverse natural areas into environments with low levels of biodiversity. Habitat loss and fragmentation of landscapes due to modern intensive farming represent the greatest threats to natural genetic diversity. Krebs et al. (1999) and Tilman et al. (2002) observed that species richness in agro-ecosystems has dramatically declined during the last decades due to the intensification of land use practices. Intensification occurs primarily at the farm level through increased use of chemicals, mechanisation and frequent cultivation to boost production per unit area. At a landscape level, the aggregation of intensively managed arable fields together with land consolidation has resulted into a transformation of
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formerly complex landscapes with relatively high proportions of (semi) natural habitats to simple landscapes dominated by arable fields.
Traditionally, people have protected their natural resources by balancing extraction, regeneration and conservation. Due to ever-increasing population and poverty, many people are forced to degrade their productive base by cultivating on marginal areas, clearing of woodlands and grasslands for arable land and reducing fallow periods that forfeit the future benefits gained from allowing fragile soils, pastures and forests to regenerate. There is also an emergent demand for degraded land for J. curcas production.
Again, massive environmental transformations occur through experimental interventions that lack adequate controls and with little recognition of the consequences (Dallmeier and Comiskey, 1998). UNEP (2005) observed that species are known to have survived the many alterations to habitat caused by natural events but are less able to adapt to natural changes at local scales when those changes are compounded by human-induced alterations such as land clearing for crop production. Ultimately, how societies manage their resources will determine how much diversity will survive. Societies vary in the extent to which they use external inputs in resource management.
In Malawi, a clear distinction can be made between a subsistence farmer who entirely depends on natural resources with low inputs and a commercial farmer who depends on external inputs (Chirwa, 2004). Subsistence farms are considered semi-natural in that farmers deliberately leave some high value species or patches of natural vegetation within the fields. Furthermore, subsistence farmers practice mixed cropping which inherently enhances agro-biodiversity. However, Stockbridge (2006) noted that the major 13
environmental problem caused by smallholder farmers arise from unsustainable practices. He cites soil erosion and loss of fertility as a result of smallholder farmer’s efforts to intensify production by adding labour to existing agricultural land without corresponding increase in capital (chemical inputs, organic matter, equipment, land conservation infrastructure). They also may cause loss of biodiversity and damage natural ecosystems as they seek to expand area under agriculture production by clearing forests and fragile ecosystems.
Commercial farmers, on the other hand, use modern technologies which tend to increase yields in the short-term. However, without regulation, this may lead to long-term profound and unpredicted consequences on biodiversity (Matson et al., 1997; Stockbridge, 2006). Intensive agriculture is characterised by frequent tillage which manipulate the soil system and high doses of chemicals and herbicides disturb natural habitats and lead to decrease in diversity (McNeely et al., 1995). Commercial farmers usually practice monocropping which leads to homogenisation of the landscape.
Studies have shown that human activities negatively influence variations in composition and abundance of woody species (Mwase et al., 2007). Local communities living in areas where significant biodiversity resources are found have used the resources for thousands of years. As such, their culture and knowledge are deeply rooted in the environment on which they depend. Through their traditional practices and knowledge, local communities have made substantial contributions to conservation and sustainable use of biodiversity. The Convention on Biological Diversity (CBD, 1992) emphasizes the importance of
14
working with local peoples to respect, preserve, and maintain traditional knowledge relevant for the conservation and sustainable use of biodiversity.
Biodiversity patterns have been found to be related to climatic factors and models have been developed to predict such relationships. It has been noted by bio-geographers and ecologists that regions do not only differ in number of species but also in the composition of these species. Early studies showed that the pattern of biodiversity might be set up in different scales and environmental gradient. Many researchers concluded that environment was the most important for the establishment of biodiversity (Qing-gui et al., 2006). Species diversity increase with increase in annual average temperature and decreasing latitude gradient such that the number and composition of species has been observed to decrease from the equator to poles. The shifts in patterns of annual and seasonal rainfall, edaphic features, and changes in land cover also affect the composition of plant communities (Nochur and Muller, 2005).
Muller et al. (2003) observed that in developing countries, it is often difficult to take complete biodiversity inventories. Taxon-based mapping methodologies are useful in predicting biodiversity patterns based on abiotic factors such as climate, soils and altitude. Nochur and Muller (2005) noted that areas that receive heavy but unevenly distributed rainfall are more likely to have smaller and less diverse epiphyte communities than areas with less total but more evenly distributed rainfall.
15
2.3
Household decision to change land use
This study has adopted the framework by Pattanayak et al. (2003) who conducted a metaanalysis of 120 adoption of agricultural and forestry technologies. They employed household production theory and developed a framework for categorizing the determinants of household decisions.
One of the objectives of the study is to identify factors affecting household decision to grow J. curcas. Clement and Amezaga (2008) identified three levels of decision making in land use change, namely, operational (household), collective choice (group) and constitutional. Lambin and Geist (2007) categorises the causes of land use change into two: proximate and underlying. The former operates at the local level (individual, household or community) and explains how and why land use is modified directly by humans. On the other hand, the latter originates from regional or global levels and explains the broader context and fundamental forces underpinning local actions. Lambin and Geist (2007) observed that household level decisions are important in influencing land use change at the local spatial scale. As such a thorough understanding of both how people make decisions and how specific environmental and social factors influence these decisions is crucial in identifying the causes of land use change.
Franzel (1999) noted that farmer’s acceptability of a technology, practice or system is the principal indicator of decision potential. Farmers’ decisions whether to change land use pattern or not depends on the perception as to whether there are more advantages than disadvantages. Acceptability encompasses biophysical feasibility, economic profitability and social suitability. Feasibility refers to whether farmers have the required technical
16
information or resources such as labour and are able to plant and maintain J. curcas plantations (Mbaga-Semgalawe and Folmer, 2000). From the farmers’ perspective, profitability refers to whether financial benefits obtained from planting J. curcas are higher than from alternative land uses (Mbaga-Semgalawe and Folmer, 2000). However, the respondents to be interviewed may not be in a position to provide such information because the revenues have not been realised yet. Therefore this study will not explicitly consider benefits. It will take net benefits implicitly. The expected benefits are assumed to be correlated with farmers’ perception of the project. The costs of planting J. curcas will be assumed to be constant over the farmers. Farmers who expect net benefits are assumed to accept J. curcas cultivation.
Vedeld (1990) and Mbaga-Semgalawe and Folmer (2000) observed that differences in adoption decisions among communities reflects the differences in their preferences or utility that are conditioned by heterogeneity in socio, cultural and economic characteristics. Chinangwa (2006) found that households that accept innovations tend to have similar characteristics. For instance, they may have frequent contact with extension workers, higher levels of education, positive attitude to change, and relative income and standard living. Pattanayak et al. (2003), Mercer and Pattanayak, (2003), Lee (2005), and Doss (2006) through meta analysis of forestry, agricultural and agroforestry studies, concluded that there is no general pattern of influence of factors on the decision. The significance of individual variables in any one study is likely to depend on the specific nature of the technology being introduced.
17
Jatropha curcas is promoted to protect soil from erosion and reclaim degraded areas. Most soil conservation programs have sought voluntary conservation practice adoption by farmers. Studying factors influencing rural farmers’ decision to invest in soil conservation found that financial factors including income are the most important (Noris and Batie, 1987). Other important factors include perception of soil erosion problem, education level, off farm employment and land tenure. They found that other factors influence only one soil conservation practice but not the other. For instance, age and race were related to conservation tillage but not other practices. The study pointed out that programs developed to enhance adoption of conservation need to take into account the special needs of resource limited farmers. However, benefits accrued from soil conservation are difficult to account for in economic terms hence economic theories based on utility maximisation may not be relied upon much. However, research exist that associates farmer adoption of conservation practices to various socioeconomic factors (Feder et al., 1981). Hence, it is argued that special studies have to be carried out to draw factors specific to the technology.
Most studies categorise adoption as dichotomous and such analysis does not indicate the extent of adoption. In practice, a technology may be adopted fully or partially by the farmers. Schutjer and van der Veen (1977), after a comprehensive review of adoption studies, concluded that the major technology issues relate to the extent and intensity of use at the individual farm level rather than to the initial decision to adopt a new practice. Hall and Khan (2003) asserted that the contribution of a new technology to economic growth can only be realized if it is widely diffused and used.
18
CHAPTER THREE 3.0 METHODOLOGY
3.1
Study site
The study was conducted in Kasungu and Mzimba districts in the central and northern regions of Malawi (Figure 1).
Figure 1: Map of Malawi showing study sites for Jatropha curcas
19
Annual temperature and rainfall ranges for the 20 year period (1990 to 2009) are presented in Appendix 5. The data was obtained from the Department of Climate Change and Meteorological Centre of Malawi. The average annual rainfall for Mzimba has been 835 mm and 774 mm for Kasungu. Mzimba has been receiving stable annual rainfall while Kasungu has had erratic rains with seven out of twenty years having total rainfall of less than 600mm. Minimum and maximum temperatures have for the past 20 years been higher in Kasungu than in Mzimba. The annual mean minimum and maximum temperatures for Kasungu versus Mzimba has been 15.8 mm versus 15.1 mm and 27.9 mm versus 26.4 mm respectively.
Mzimba and Kasungu districts are among the most active districts in agricultural production which is the primary source of livelihood for farming families and the back bone of Malawi’s economy. The major crops grown include tobacco which is the major export earner and maize which is the staple food. Clearing of forests for crop land has been cited as one of the major environmental challenges the country is facing. Tobacco curing has been singled out as the chief cause of deforestation especially in Kasungu (Tobin and Knausenberger, 1998). Tobacco production coupled with high population density of 78 persons per square km and has led to high deforestation in Kasungu than in Mzimba whose population density is 70 persons/sq. km (NSO, 2008). According to NSO data Mzimba has a population of 724,873 people while Kasungu has 616,085 people. There are 142,980 and 127,265 households with average household size of 5.2 and 4.5 in Kasungu and Mzimba districts, respectively.
20
Mzimba and Kasungu districts were purposively chosen on the basis that households with support from Bio Energy Resources Ltd (BERL) committed their unproductive land to J. curcas plantation. Farmers with large parcels of land like most people in Mzimba District established pure J. curcas plantations on individual basis whereas those with small landholdings intercrop it with food or other cash crops as hedge rows or boundary planting. In areas such as Kasungu where there are large tracts of communally owned marginal and degraded lands, communities establish communal plantations. The contracting companies such as BERL offer technical and material support during establishment phase and will be buying seed at centralised markets. BERL works with a club (comprised of people within the same village or locality), which are aggregated into clusters comprising of five clubs each. Several clusters are formed in an Extension Planning Area (EPA).
Data were collected in two phases. First, a reconnaissance survey was conducted in November 2008 to obtain general idea of J. curcas cultivation. Key informant interviews with village chiefs, and BERL planting technicians coupled with field observations revealed the extent of marginal and degraded areas. This also helped to have an overview of socio-economic profiles of households in the study areas. Second, two primary data sets were collected in March, 2009.
3.2
Plant species Diversity
3.2.1 Sampling framework, design and sample size. The sample frame for enumeration of plant species was marginal and degraded areas earmarked for J. curcas cultivation which was obtained from BERL offices in Mzimba 21
and Kasungu districts. In both districts, fallow and adjacent uncultivated areas were delineated using historical knowledge of the farmers. In Kasungu, enumeration of plant species was done within both subsistence farmers’ land and commercial estates. According to van Oarschot et al. (2008), differing levels of human intervention can be used to reconstruct a scientific baseline of historic conditions which, in this study, were considered to be uncultivated areas (no tillage) and subsistence farms (low input). These two were used as a baseline for assessing the effect of cultivation and intensified farming on diversity of plant species. Marginal and degraded areas are remote and patched. As a result, variation at local scale is high. Therefore, the survey used two-stage cluster sampling to draw sample units (Reed and Mroz, 1997; Newton, 2007). First, in each district, villages were randomly selected from the list of participating villages. Second, sub-samples of subplots (patches of marginal and degraded areas) within cultivated and uncultivated areas were randomly selected for the survey. In Kasungu, both commercial and subsistence landscapes were sampled to assess the effect of intensified farming. A group of smaller homogeneous units (in terms of degree of anthropogenic activities) was taken together to make up the sampling unit (site class). Based on the district (second column), farming system (third column) and cultivation (fourth column), six site classes (first column) were arrived at as presented in Table 1. Following Newton (2007), a fixed area sampling approach was used in which rectangular plots of 10m X 20m were laid to capture habitat heterogeneity. The number of plots was determined proportional to patch size. At a sampling intensity of 2%, a total of 39 plots were laid as shown in Table 1. Plots were laid at a distance of 250m from one another 22
and 50m away from roads, gardens or homes. The starting point of plot was determined randomly and a 20m base line was laid following true north campus direction. The plots were extended on either side of the base line by 5m. Global Positioning System (GPS) was used to record coordinates of the plots taken at the centre. The plots were overlaid on national map themes obtained from Malawi’s National Spatial Data Centre (NSDC) using Arc View GIS (Applegate, 2002).
Table 1: Site classification using district, farming system, and cultivation status Site class
District
Farming system
Cultivation
No. of plots
1MSC
Mzimba
Subsistence
Fallow
11
2MSU
Mzimba
Subsistence
Uncultivated
7
3KSC
Kasungu
Subsistence
Fallow
8
4KSU
Kasungu
Subsistence
Uncultivated
4
5KCC
Kasungu
Commercial
Fallow
6
6KCU
Kasungu
Commercial
Uncultivated
3
Total
39
Note: 1MSC=Mzimba subsistence cultivated, 2MSU=Mzimba subsistence uncultivated, 3KSC=Kasungu subsistence cultivated, 4KSU=Kasungu subsistence uncultivated, 5KCC=Kasungu commercial cultivated, 6KCU=Kasungu commercial uncultivated 3.2.2 Data collection for species diversity Plant species data was collected through a rapid biodiversity assessment (RBA) using visual encounter survey (VES) (Stork and Samways, 1995). During the VES all plants (including large trees, regenerants, saplings, shrubs, and herbs) in the plot were identified by their scientific names or local names, counted and recorded (Appendix 1). Local people helped to identify species with local names and the corresponding scientific names were checked in the Dictionary of Plant Names in Malawi (Binns, 1972). Samples of
23
unidentified plants were collected using temporary plant press and later identified by a botanist from Forestry Research Institute of Malawi (FRIM) with reference to Flora by Blundell (1987), Palgrave (1988) and White et al. (2001).
Data on climatic and edaphic conditions of the study sites were also collected. Temperature and rainfall data for the past 20 years was obtained from Malawi Meteorological Services. Soils were sampled at two random points near the centre of the plots laid for enumeration of plant species, taken at 0-30 cm depth (including the deep humus layer but excluding recognizable plant remains). The samples were then air-dried and stored in plastic bags prior to analysis at Bunda College Soil and Plant Analytical Laboratory. The following chemical measurements were done: pH, percent of organic matter, calcium, magnesium, nitrogen and phosphorus.
3.2.3 Measurement of plant species diversity Biological diversity is defined as the variability among living organisms from all sources, including inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part. This includes diversity within species, between species and of ecosystems (CBD, 1992). Between species diversity has been characterised by measures of species richness, abundance and diversity (Bisby, 1995; Purvis and Hector, 2000). Species richness (also called α-diversity) measures the number of species within an area, giving equal weight to each species. Counting the number of taxa in the sample under consideration is always the first step especially in unexplored regions (Purvis and Hector, 2000). Species richness is criticised for not making use of relative abundances hence being less informative, it is nonetheless argued to be the simplest way of
24
describing a population. It is often used as the first pass estimate of diversity for a community. Species diversity measures the number of species found in an area adjusting for both sampling effects and species abundance (Bisby, 1995). Often, the individuals are not evenly distributed among species. A site containing dozens of species may not seem particularly diverse if 99.9% of the individuals belong to the same one species. Evenness is defined as the ratio of observed diversity to maximal possible diversity if all species in a sample were equally abundant.
Biodiversity data was processed and analysed in Microsoft Excel, BioDiversity Professional (McAleece, 1997) and GenStat discovery edition 3 (VSNI, 2008). Descriptive statistics, diversity indices and analysis of variance were produced. T-test was used to compare the indices between site classes. Generalised linear regression using unbalanced treatment structure was employed to test for differences in tree density and basal area and shrub density among the site categories. Fisher’s least significant difference (LSD) test was used to explore significant differences between means.
3.2.3.1 Species richness Species richness S was determined as the number of species present in a patch (Siccha Rojas and Lindegarth, 2003). It does not make use of relative abundances hence it is not largely informative but it is the simplest way of describing a population. It is often used as the first pass estimate of diversity for a community.
25
3.2.3.2 Species diversity Species diversity has been measured by use of heterogeneity indices which take both evenness and richness into account. The two commonly used measures are Simpson and Shannon indices. These two differ in that Shannon is derived from information theory where as Simpson is the dominance index. Shannon index reflects species richness element of diversity and is more sensitive to changes in rare species such that failure to include all the species from the community in the sample leads to a biased estimate. Simpson on the other hand, is weighted towards the abundances of commonest species rather and is more sensitive to changes in the more abundant species (Magurran, 1983). These differences justify the use of both indices.
Simpsons index, Ds
Simpson’s index measures concentration of species (Simpson, 1949). It is a model that represents the probability that two individuals, picked independently at random from a population will belong to different species. Simpson's index yields values on a probability scale from 0 to 1 in ascending order with increased diversity. It is expressed as:
s
Ds = 1 −
∑ n (n − 1) i
i =1
i
N ( N − 1)
,
(1)
Where, s is the number of species, ni number of individuals belonging to the ith species N the total number of individuals in the sample.
26
Simpson index is criticised for being strongly affected by the abundance of the two or three most abundant species in a community (DeJong, 1975). DeJong indicated that a cumulative plot of Ds against S forms a plateau after the first 10 to 12 species. The index is insensitive to the relative contribution of the rare species encountered. Shannon-Weiner index, H’
The second most commonly used measure of species diversity is the Shannon-Weiner index. Shannon-Weiner index is considered an information index. The Shannon Index assumes that all species are represented in a sample and that the sample was obtained randomly. Shannon-Weiner index is expressed as:
s
H ' = C ∑ p i ln pi
(2)
i =1
where, C is a constant and pi is the proportion n i/N.
DeJong (1975) indicated that H’ also shows a plateau-effect similar to that with Ds. Franc (1998) points out that the drawback of the H’ is that it does not account for the importance of rare species. If the frequency is low say xi=10-3, then its contribution Hi=xilogx i to the index is low as well.
The variance of Ds was estimated using the formula by StatsDirect Ltd (2009): 3 2 s s ni 2 ni ni 4 N (N − 1)( N − 2)∑ + 2 N ( N − 1)∑ − 2 N ( N − 1)(2 N − 3)∑ i =1 N i =1 N i =1 N s
VarDs =
[N (N − 1)]2
27
(3)
The variance of H’ was determined using the equation by Magurran (2004) as:
∑ p (ln P ) − (∑ p ln p ) Var H ' =
2
2
i
i
i
i
N
−
S −1 2N 2
(4)
and the 95% confidence interval for the diversity of a site category was determined by
CI = H ' ± 1.96 Var H '
(5)
The significant differences between any two site categories were tested using t-test (Magurran, 2004).
t=
H 1' − H 2'
(Var H
' 1
+ Var H 2'
)
(6)
1/ 2
Where, H’1 is the Shannon-Weiner index of site class 1 and Var H’1 is its variance. The degrees of freedom were calculated using the equation:
(Var H + Var H ) df = (Var H ) / N + (Var H ) ' 1
' 2 1
1
' 2 2
' 2 2
(7)
/ N2
Where, N1 and N2 being the total number of individuals in site classes 1 and 2 respectively.
28
3.2.3.3 Species evenness Pielou's evenness (equitability) index J’ is the most common measure of evenness (Siccha Rojas and Lindegarth, 2003). It is expressed as the ratio of observed ShannonWeiner value to the maximum possible diversity.
J'=
H' H' = H ' max log S
(8)
where, H' is the Shannon-Wiener diversity measure, S is the average species richness. If there is perfect equitability, then log(S) = H' and J = 1. J’ will approach 1 if H’ will approach the maximal possible value for the given set of species, meaning that all species in the sample will be equally abundant.
3.2.3.4 Species abundance K-Dominance plots are used to rank sites based on abundance of species. K-Dominance plots linear percentage cumulative abundance against log species rank from the most to the least abundant species (Pratt et al., 1984). The curves can be used to assess differences in diversity if the curves do not overlap. The upper curve will be drawn from the more dominant and hence less diverse assemblage while the lowest curve represents the most diverse community. Magurran (2004) pointed out that K-Dominance plots which intersect may be the most informative in that they illustrate the shift of dominance relative to that of species richness.
29
3.2.3.5 Beta diversity Beta diversity, which is defined as the extent to which the diversity of two or more spatial units differs, is used as an indicator of spatial heterogeneity (Magurran, 2004). In this study, the extent to which two site categories contain common species was used to characterise spatial homogeneity as cultivation and intensified farming are hypothesised to promote homogeneity. Kulezynski’s coefficient (C k) is chiefly used in assessing floristic similarity (Ceska, 1965; Kronberg, 1987; Mathieson et al., 2008) and is expressed as follows: j j + a b Ck = * 100 2
(9)
where, j is the number of species common to both site classes, a and b are respectively the total number of species in each site class. Higher C k values shows that the sites share more species in common and are more similar.
3.3
Determinants of Jatropha curcas adoption
3.3.1 Conceptual framework Adoption refers to the incidence/pattern and intensity of use of a technology (Langyintuo and Mekuria, 2005). In this study the incidence indicates whether a farmer has decided to grow J. curcas or not and intensity refers to the amount of land planted. Using the framework by Pattanayak et al. (2003), this study integrated sociological and economic models together with institutional and physical aspects to explain differences in planting behaviour among individual households. The argument being that household’s resource allocation preferences and behaviour is codetermined by utility or profit maximisation 30
and social processes and structures (Greene, 1997). Economic models based on utility or profit maximisation fail to encompass attitudinal and social variables which are also important in explaining the household decision-making process. Omission of either economic or social variables would lead to biased estimators and invalid inference procedures (Mbaga-Semgalawe and Folmer 2000). The underlying assumption is that farmers decide to plant J. curcas if they perceive positive net benefits from its cultivation. Consider U(M ji , Aji) as underlying utility function, which ranks the preference of the ith farmer for the jth landuse (j = 1, 2: 1 = J. curcas cultivation and 2 = other landuse). Thus, the utility derivable from the J. curcas cultivation is a function (Fi) of M, which is a vector of farm and farmer specific attributes of the adopter and A, which is a vector of the attributes associated with the technology. Although the utility function is unobserved, the relation between the utility derivable from a jth landuse is postulated to be a function of the vector of observed farm, farmer and landuse specific characteristics (e.g. benefits, education, etc.) and a disturbance term having a zero mean as follows: Uji =α jFi (Mi , Ai ) + eij
j= 1, 2; i = 1, 2, …, n
(10)
As the utilities Uji are random, the ith farmer will select the alternative j = 1 if U1i > U2i or if the non-observable (latent) random variable y* = U1i – U2i > 0. Let Ji be an indicator variable for adoption where Ji=1 if the farmer grows Jatropha curcas and Ji=0, otherwise. The probability that of adoption can be represented as follows:
31
Pi = Pr ( J i = 1) = Pr (U1i > U 2i )
= Pr [α1Fi (M i , Ai ) + ε 1i > α 2 Fi (M i , Ai + ε 2i )] = Pr [ε 1i − ε 2i > Fi (M i , Ai )(α 2 − α1 )]
letµ1 denote (ε 1i − ε 2 i ) and β denote (α 2 − α1 )
(11)
= Pr (µi > − Fi (M i , Ai )β )
= Fi ( X i β ) Where X is the n x k matrix of the explanatory variables and β is a k x 1 vector of parameters to be estimated, Pr(.) is a probability function, μi is a random error term, and Fi(Xiβ) is the cumulative distribution function for μ i evaluated at Xiβ. The probability that a farmer will plant J. curcas is a function of the vector of explanatory variables and of the unknown parameters and error term. The five broad categories of explanatory variables by Pattanayak et al. (2003) include: household preferences, resource endowments, market incentives, risk and uncertainty and biophysical characteristics. Household preferences include variables that measure household specific characteristics such as risk tolerance, innovativeness and household homogeneity. Many of these factors cannot be measured directly therefore, proxies such as gender, main occupation, age and education were used. Resource endowments reflect income and wealth that are either invested in the new technology or deter people from investing. Non-farm income, household labour and livestock units were considered. Market factors such as prices were not included because J. curcas did not reach economic maturity during the time of study. Risk and uncertainty such as pests and unfamiliarity were not included because farmers do not have experience of J. curcas as a commercial crop. However, inferences were made from effect of other factors such as age, education and income as they indicate ones ability to overcome risks. Tenure security was not considered because respondents owned land inherited from parents. Ownership of
32
uncultivated land and fallow were considered to be the major biophysical properties affecting adoption. Jatropha curcas is being recommended to be planted on unproductive land which in the study area is dominated by fallow and uncultivated land. 3.3.2 Data Sources Data used to analyze household decision to grow J. curcas were collected from Mzimba and Kasungu using a household survey. The sample of households for the study was derived from a multistage stratified probability sampling design. First, EPAs were purposively chosen. Second, six clusters were randomly selected from the selected EPAs. Two villages in each cluster were randomly selected for the study making a total of 12 villages. Households within the sampled villages were then stratified into two based on whether they grow Jatropha curcas (JP) or not (NJP). An ordered list of those that have made a decision was obtained from BERL district offices and the list for entire villagers from agricultural extension and development officers (AEDO).
The number of
households in each stratum was determined proportional to the size of the stratum. Individual households interviewed were selected using systematic sampling procedure and the sample size(s) was determined using the formula by Krejcie and Morgan (1970): s = x 2ΦP(1 − P ) ÷ d 2 (Φ − 1) + x 2 P(1 − P )
(12)
where x2 is the table chi-square for 1 degree of freedom at the desired confidence level (3.841); Ф is the number of households (142,980 in Mzimba and 127,265 in Kasungu) (NSO, 2008); P is the population proportion assumed to be 0.50 since this provide the maximum sample size (Krejcie and Morgan 1970) and was close to the proportion of adopters in the sampled villages; and d is the degree of accuracy expressed
33
as a proportion (0.1). The calculated sample sizes were 117 households in Mzimba and 101 households in Kasungu. In Mzimba, the proportion of adopters in the sampled villages was 55% while in Kasungu it was 49.5%. A 10% of the calculated sample size was added for incidence of non-response.
3.3.3 Data collection and analysis
A team of trained interviewers and a supervisor administered a structured questionnaire which was first pretested and inconsistencies were rectified (Appendix 2). The questionnaire had two categories: household socio- demographic condition, which was characterized by six variables namely household head’s gender, marital status, main occupation, age, education and household size; economic condition of the household which was measured by four different variables namely non-farm income, household labour and livestock ownership. These variables correspond to the three main types of incomes for households in the rural areas in study districts. Ownership of unproductive land (fallow and uncultivated areas) was also elicited.
Household socioeconomic data was processed and analysed in Microsoft Excel, Statistical Package for Social Scientists (SPSS Inc., 2006) and Stata (Stata Corp., 2007) to produce descriptive statistics, cross tabulations, frequencies and probabilities. Descriptive statistics generated socioeconomic profiles of the sample households. Frequencies were used to estimate the percentage of the population that have chosen to plant or not to plant J. curcas. Mean estimates of a choice to change or not provided decision by similar group of individuals. The cross tabulation of the decision to plant and
34
socioeconomic data revealed the expected effects from decision theory, confidence in data and insight into the factors that determine individuals decision to plant J. curcas.
3.3.4 Analytical Models Several models have been employed in the analysis of adoption. The initial binary decisions have been analysed using Logit (logistic) and Probit (normal) models while extent of adoption has been modelled using Tobit and Heckman models (Gujarati, 2004). Although Logit and Probit models produce different parameter estimates, with estimates of Logit roughly π / 3 times larger than those of the corresponding Probit model, the estimates end up with the same standardised impacts of independent variables (Hun Myoung, 2009). Hun Myoung points out that the Logit model is widely used because its estimation reaches convergence fairly well. Hence, the binary logistic model was used to estimate the choice function that relates respondent’s answer to the socioeconomic characteristics of the respondent. The dependent variable is dichotomous and the estimated Y values, which are conditional probabilities, lie within the (0, 1) interval (Greene, 2003; Gujarati, 2004).
The Tobit model after Tobin (1958) has been considered for the assessment of extent of adoption as indicated by amount of land a farmer allocate to J. curcas cultivation in relation to socio-economic conditions. Although Tobit models are used to assess the decision, Barry (2005) argued that they yield higher probabilities because they are sought on one side of the cumulative distribution function and recommended the use of Logit or Probit model in the analysis of probabilities.
35
Extent of J. curcas cultivation was studied only in Mzimba because the dependent variable is exclusively observed in Mzimba where more households had large tracts of land and plant on individual basis. For estimating the extent of J. curcas cultivation, Tobit model has been preferred to OLS because it allows for inclusion of observations which have zero J. curcas acreage. Constrained OLS estimation based on a censored sample would yield inconsistent estimates (Gujarati, 2004). Moreover, OLS of observations for which acreage is greater than zero would result in sample selection bias in the estimated coefficients. Ervin and Ervin (1982) observed that Tobit models are used on assumption that the same set of factors has the same influence on the adoption decision and effort which may not be the case. Heckman (1979) offers an alternative procedure which would allow for different factors influencing adoption and effort. The two equation procedure involve estimation of a probit model of the adoption decision, calculation of sample selection bias, and incorporation of that bias into a model of effort estimated with OLS.
Moreover, Norris and Batie (1987) observed that while Heckman’s procedure allows for different model specifications for adoption and effort, it does not allow for the decomposition of elasticities afforded by the Tobit procedure. Norris and Batie, therefore, assert that Tobit remains the more appropriate model for determining policy implications. Consequently, Tobit, other than Heckman, has been widely used in studying extent of adoption (Akinola and Young, 1985; Nkonya et al., 1997; Rajasekharan and Veeraputhran, 2002; Barry, 2005; Chukwuji and Ogisi, 2006).
36
3.3.4.1 Logit Model The logit model was employed to understand how household make initial decision to plant J. curcas. From the probabilities:
Pi = probability that ith household plants J. curcas 1 – Pi = probability that it does not plant J. curcas
The Logit Model based on cumulative logistic probability function is: Li = ln (Pi / 1-Pi) = α + βXi + εi
(13)
Where Li = Adoption of J. curcas (1 = J. curcas planter (JP), 0 = non J. curcas planter (NJP) ln = natural logarithm Pi and (1-Pi) as defined above α = constant term β = vector of parameters to be estimated (coefficients) Xi = vector of household’s attributes associated with choice to plant J. curcas (see Table 2). εi = error term.
37
Table 2: Specification of the predictor variables used in binary logistic and Tobit models and direction of influence (signs) Variable
Expected
Description
sign SEX
+
dummy variable for gender of the household head (1 for male, 0 female headed households) male headed are more likely to decide to plant J. curcas than their female counterparts.
MOCCUP
+
dummy variable for main occupation (1 for farmer, 0 otherwise). It is highly probable that farming households plant J. curcas than households who rely on other activities for a livelihood.
AGE
-
age of household head (years). Older household heads are less probable to plant J. curcas than younger ones.
EDUC
-
formal education of the household head (years). Household head that attained higher education are less likely to plant J. curcas.
ADULTS
+
number of adult members in the household. A household with more adult members is more likely to plant J. curcas.
HHLUs
+
number of livestock units (LU) and conversion of cattle, sheep, and goats into livestock units was done using the following identities: 1.2 cows = 1 LU; 5 sheep = 1 LU; 4 goats = 1 LU (Agrawal and Gupta, 2005). Farmers with more livestock units are more likely to plant J. curcas than farmers with fewer animals.
INCOS
+
annual household income from sources other than farming (MK). A household with substantial income from other sources is more likely to plant J. curcas than households with less non-farm income.
FALLOW
+
dummy variable for ownership of fallow land (1 = yes, 0 = no). It is highly probable that farmers that own fallow land will plant J. curcas
UNCULT
-
dummy for ownership of uncultivated area (1 = yes, 0 = no). It is less likely that farmers with uncultivated land will plant J. curcas
38
3.3.4.2 Tobit model A Tobit model after Tobin (1958) was used to analyse the extent of J. curcas cultivation in relation to socioeconomic factors. The dependent variable have censored distribution: area planted with J. curcas is zero for those not planting. From the theoretical framework the Tobit model is explained by the threshold concept as,
Yi = Yi* = Xiβ + Ui
if Xiβ + Ui > Yi *
Yi = 0,
if Xiβ + Ui ≤ Yi*
i = 1, 2, ..., n
(14)
where, Yi = dependent variable for the ith observation (area planted with J. curcas); Yi* = underlying latent dependent variable for the ith observation; Xi = vector of explanatory variables corresponding to the ith observation (see Table 2). β = vector of unknown parameters associated with the explanatory variables; Ui = error term assumed to be independently distributed as N (0, σ2); n = number of observations.
Tobin indicated that the expected value of the level of technology adoption E(Y) to be made by new adopters of the technology is given by: E(Y) = XβF(z)+ σf(z)
(xv)
Where δ (sigma) is the standard error of the estimate, z is the z-score for an area under the
normal curve estimated at mean values of Xi given as
39
α +
n
∑
i =1
σ
X iβi
, f(z) is the unit
normal density, and F(z) is the cumulative normal distribution function which predicts the probability of adoption of technology given the mean value of the explanatory variables. That is, the percentage chance of a technology being used by new adopters. The expected level of adoption by those using the technology, Y*, is Xβ plus the truncated normal error term (Amemiya, 1973) given by: E(Y*)= Xβ+σf(z)/F(z)
(xvi)
Consequently, the basic relationship between the expected value of all observations, E(Y), the expected value condition of being above the limit, E(Y*), and the probability of being above the limit, F(z) is: E(Y)=F(z)E(Y*)
(xvii)
Mcdonald and Moffit (1980) decomposed the effect of a change in the ith variable of X on Y using partial derivatives as: ∂E (Y ) = F(z). ∂E (Y *) + E(Y*). ∂F ( z ) ∂X i ∂X i ∂X i
(xviii)
and the derivatives were calculated as follows (Mcdonald and Moffit, 1980): ∂E (Y *) = β[1-zf(z)/F(z) – f(z)2/F(z)2 ∂X i
and
40
∂F ( z ) = f(z)β/σ ∂X i
CHAPTER FOUR 4.0 RESULTS This chapter presents results from the analysis of plant species diversity which includes richness, abundance, diversity and evenness. This section is followed by results from analysis of household decision to plant Jatropha curcas on unproductive areas. 4.1
Plant species diversity
4.1.1 Species richness The study found that fallow and uncultivated areas are floristically rich in taxa. The total number of species in 39 plots of 0.02 ha was 21 families, 39 genera and 54 species of trees (diameters >5 cm); 41 families, 78 genera and 98 species of shrubs and regenerants/ saplings; 27 families, 47 genera and 58 species of herbaceous plants (Table 3). Table 3: Richness of trees, shrubs and herbaceous plants (per hectare) within different farm systems and cultivation status Trees (dhb >5cm)
Shrubs/regenerants
Herbs
Site class
Family
1MSC
9
11
16
23
41
47
13
16
21
2MSU
10
21
24
20
32
37
6
9
12
3KSC
10
15
17
13
23
29
10
14
17
4KSU
12
19
20
19
26
31
3
5
8
5KCC
0
0
0
15
21
23
13
21
22
6KCU
11
17
19
9
11
12
4
6
9
Total
21
39
54
41
78
98
27
47
58
Genus Species Family Genus Species Family Genus Species
Note: 1MSC=Mzimba subsistence cultivated, 2MSU=Mzimba subsistence uncultivated, 3KSC=Kasungu subsistence cultivated, 4KSU=Kasungu subsistence uncultivated, 5KCC=Kasungu commercial cultivated, 6KCU=Kasungu commercial uncultivated
41
Table 3 shows that trees were more prevalent in uncultivated areas than on fallow land but also on subsistence landscapes than on commercial landscapes. No trees were recorded in commercial fallow areas. Fallow areas were richer in herbaceous plants compared to uncultivated areas. Comparatively, commercial cultivated landscapes were richer in herbs than subsistence cultivated landscapes. Similarly, commercial uncultivated were richer in herbs than subsistence uncultivated landscapes. 4.1.2 Species abundance Species abundance curves (Figure 2) shows that site class 1MSC had the highest diversity while 6KCU had least diversity of tree and shrub species. In general, diversity was lower in commercial areas than subsistence sites. Cultivated areas had relatively higher diversity than in adjacent uncultivated areas.
Figure 2: K-Dominance showing the diversity of woody plants within the six site classes As shown in Figure 3 below, there was no clear pattern of diversity of herbaceous plants. However, more species with lower numbers were noted in cultivated areas as shown by the extended curves for 5KCC and 3KSC. The lower curves for 3KSC, 1MSC and 5KCC 42
indicate that cultivated areas had some species that were comparatively more abundant than uncultivated areas.
Figure 3: K-dominance plot showing the diversity of herbaceous plants
Figures 4a-e show that the site classes differ in terms of abundance of shrub and regeneration species. In Mzimba, it can be observed that species that had abundant regeneration and shrubs (>2500 stems/ha) in uncultivated areas had relatively fewer stems within cultivated areas (Figure 4a). The species include Zanha africana, Byroscarpus olientalis, Brachystegia speciformis, Vernonia adoensis and Neorautanenia kirkii. Abundant species in cultivated areas include Temnocalyx obovatus, Burkea africana,
Julbernardia
Catunaregum
spinosa,
globiflora,
Brachystegia
Diplorhynchus
utilis,
condylocarpon,
43
Brachystegia
manga,
Aeschynomene
cristata,
Friesodielsia obovata and Parinari curatellifolia which were a few within uncultivated areas.
In Kasungu within subsistence landscapes (Figure 4b), the most dominant species in uncultivated areas were D. condylocarpon, J. paniculata, Faurea saligna and Brachystegia boehmii. Diplorhynchus condylocarpon despite being the most abundant in uncultivated areas had no regenerants within cultivated areas. Brachystegia manga, Dalbergia nitidula, C. spinosa, Randia spp. and J. paniculata were few within uncultivated but abundant in cultivated areas. In Kasungu within commercial landscapes (Figure 4c) a similar trend has been observed where abundant species in uncultivated areas are represented by a few stems in cultivated areas. C. spinosa, B. boehmii, Annona senegalensis and P. curatellifolia were the most abundant in woody areas. On the other hand Myrothamnus flabellifolius, D. condylocarpon, B. boehmii Eriosema psoralcoides, Markhamia obtusifolia, Bauhinia petersiana and P. curatellifolia were abundant in cultivated areas. In Kasungu, a comparison between commercial and subsistence landscapes within cultivated and uncultivated areas revealed differences in terms of most abundant species. Cultivated subsistence fields had different set of species that were abundant as compared to the ones within commercial fields (Figure 4d). Similarly, uncultivated areas within subsistence had a different set compared to those within commercial landscapes (Figure 4e).
44
Abundance (no.)
200 180
(4a)
160 140
1MSC
120
2MSW
100 80 60 40 20 0
Species
Abundance (no.)
160 140 120
3 K SC
(4b)
100
4 K SW
80 60 40 20 0
Species
Abundance (no.)
200 180 160 140
(4c)
5KCC
120
6KCW
100 80 60 40 20 0
Species
45
Abundance (no.)
160 140
(4d)
120
3KSC 5KCC
100 80 60 40 20 0
Species
Abundance
250 200 150
(4e)
4KSW 6KCW
100 50 0
Species Figure 4: Species abundance (number of plants times 50 ha) plots between cultivated 1MSC and uncultivated 2MSU in Mzimba (a); cultivated 3KSC and uncultivated 4KSU subsistence landscapes in Kasungu(b); cultivated 5KCC and uncultivated 6KCU commercial landscapes in Kasungu (c) and between subsistence and commercial farms in Kasungu that were cultivated (d) and uncultivated (e). The bars represent the abundance of species sorted from the most abundant from the right. Species that were not found in the base classes were sorted in descending order from the left.
46
4.1.3 Species diversity of trees and shrubs and of herbaceous plants Table 4 shows Shannon-Weiner (H’) Simpson’s (Ds) and Pielou’s equitability (J’) indices of tree and shrub species and of herbaceous plants. In Mzimba, cultivated areas were more diverse in tree and shrub species but with equal evenness compared to uncultivated areas. In terms of herbaceous plants, cultivated areas were more diverse and more even than uncultivated areas. In Kasungu, subsistence landscapes had higher diversity and more evenness of tree and shrub species than commercial landscapes. Cultivated areas were more diverse and more even in terms of both woody and herbaceous species than uncultivated areas in both subsistence and commercial agricultural landscapes. Table 4: Species diversity and evenness within different farm systems and cultivation status Landuse
Trees & shrubs
Herbaceous plants
category
H' (nats ± SE)
Ds (nats ± SE)
J'
H' (nats ± SE)
Ds (nats ± SE)
J'
1MSC
3.33±0.055a
0.948±0.007
0.82
1.48±0.016
0.584±0.003
0.504
2MSU
2.96±0.090b 0.928±0.016
0.82
1.16±0.019
0.546±0.007
0.427
3KSC
3.01±0.069b 0.931±0.011
0.82
1.77±0.013
0.768±0.003
0.625
4KSU
3.10±0.119b 0.917±0.023
0.79
0.84±0.039
0.338±0.018
0.406
5KCC
2.66±0.100c
0.908±0.023
0.84
1.46±0.009
0.708±0.002
0.473
6KCU
2.28±0.062d 0.870±0.018
0.69
1.22±0.021
0.615±0.009
0.553
Note: 1MSC=Mzimba subsistence cultivated, 2MSU=Mzimba subsistence uncultivated, 3KSC=Kasungu subsistence cultivated, 4KSU=Kasungu subsistence uncultivated, 5KCC=Kasungu commercial cultivated, 6KCU=Kasungu commercial uncultivated
47
4.1.4 Floristic similarity between study site classes Results of Kulezynski’s coefficients of floristic similarity, which indicates the extent to which two site categories contain common species, are presented in Table 5. Highest species similarity was observed in Mzimba with more than 60% tree and shrub species common to both cultivated and uncultivated areas. A few species (21.58%) were shared between subsistence uncultivated areas (four) and commercial cultivated areas (five) in Kasungu. Commercial cultivated areas shared relatively fewer species (
