NORWEGIAN UNIVERSITY OF LIFE SCIENCESDEPARTMENT OF INTERNATIONAL ENVIRONMENT DEVELOPMENT STUDIES (NORARGIC)MASTER THESIS 30 CREDITS 2007
POTENTIAL FOR SOIL CARBON SEQUESTRATION THROUGH REHABILITATION OF DEGRADED LANDS IN THE BARINGO DISTRICT, KENYA
RUNE STENE
The Department of International Environment and Development Studies, Noragric, is the international gateway for the Norwegian University of Life Sciences (UMB). Eight departments, associated research institutions and the Norwegian College of Veterinary Medicine in Oslo. Established in 1986, Noragric’s contribution to international development lies in the interface between research, education (Bachelor, Master and PhD programmes) and assignments.
The findings in this thesis do not necessarily reflect the views of Noragric. Extracts from this publication may only be reproduced after prior consultation with the author and on condition that the source is indicated. For rights of reproduction or translation contact Noragric.
© Rune Stene, June 2007 [email protected] Noragric
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Declaration
I declare the originality of my work, and acknowledge the use of all materials other than my work. This work has not been submitted to any other University than UMB for any type of academic degree
ÅS, 15.06.2007
Rune Stene
Acknowledgement
This study is a part of the project “Land rehabilitation and carbon sequestration in the lowlands of Baringo District, Kenya”, a collaboration project between the International Centre for Research in Agroforestry (ICRAF) and Rehabilitation of Arid Environments Trust (RAE), managed by the United Nation Development Programme (UNDP).
The present thesis is the final part of my Masters degree in Management of Natural Resources at the Norwegian University of Life Sciences (UMB). Firstly, I would like to thank my supervisors Kjell Esser (Professor at Department of International Environment and Development Studies, UMB), Johannes Deelstra (Senior Researcher at the Norwegian Institute for Agricultural and Environmental Research (Bioforsk)). They have supported me and given me helpful advices throughout my work. I would also like to thank Murry Roberts and Dr. Elisabeth Meyerhoff (founders of the Rehabilitation of Arid Environments Trust) for great assistance and kindness during my fieldwork in Baringo. In field I had the pleasure of working with William Chebii, (RAE Field Assistant), Alex Koipiri and Charles Chelagat.
Finally, I want to give a special thank to Tor-Gunnar Vågen (Dr. at International Centre for Research in Agroforestry (ICRAF)) for being very helpful during my stay in Kenya. He has also been very supportive during the writing process.
Abstract
As a part of the United Nation Development Programme (UNDP) project “Land rehabilitation and carbon sequestration in the lowlands of Baringo District, Kenya” this study’s main purpose was to examine the sequestration potential. By sequestrating atmospheric carbon, soils can function as a sink for fossil fuel emissions. If good sequestration results are proven, the next step is to determine if a financial flow through the carbon quota market (Quito-quota) could generate income to the local rehabilitation work, which may lead to circle of rehabilitation acts. The Rehabilitation Arid Environments Trust (RAE) have for more than 20 years been teaching and training farmers in the Baringo District in Kenya, to reclaim and manage degraded areas by fencing them in (enclosures) and introducing various rehabilitation intervention measures such as soil and water conservation, tree planting and introduction of new and improved grass species. The aim of this study was to examine these enclosures to assess their carbon sequestration potential and changes in their soil organic carbon (SOC) stocks. It is expected that the SOC level will be higher inside the rehabilitated fields than outside, due to a higher carbon sequestration in the reclaimed fields. Paired samples were taken from inside and outside the enclosed intervention areas. To supplement the soil records, vegetation measurements, infiltration measurements and bulk density tests were conducted, using the Land Degradation Surveillance Framework (LDSF) developed by ICRAF. Soil organic carbon stocks range from 4.0 to 27.4 Mg C ha-1 (1.2–10.7 g C kg-1). The results from the soil analyses (Vis-NIR spectroscopy) show no significant increase in SOC in rehabilitated areas after controlling for landscape position. Rehabilitated areas however, show a significant higher infiltration capacity and lower bulk density. Soil organic carbon content tends to be highest in woody vegetated areas, while where shrub densities and infiltration rates are high, bulk density and sand content is low. The sequestration potential in the Baringo District is found to be 0.14-0.41 Mg C ha-1 yr-1, with a net SOC gain in a period of 20-50 years estimated to 240-1765 Mg C (86 ha). Within a large number of carbon sequestration strategies the most effective approach is most likely to be the one that creates win-win situations, were the local society experience an improved livelihood through increased ecological services.
Sammendrag
Som en del av United Nation Development Programme (UNDP) prosjektet “Land rehabilitation and carbon sequestration in the lowlands of Baringo District, Kenya” har denne studien undersøkt karbon sekvestreringspotensialet i Baringo området. Ved karbon sekvestrering kan jordsmonnet fungere som et langtidsdeponi for CO2 utslipp fra fossilt brensel. Om denne studien oppnår gode resultater vil man videre undersøke muligheten for å kunne gi et større områder en salgbare karbon kvote(Quito-kvote). Eventuelle inntekter vil gå inn i den videre rehabiliteringen av de forfalte områdene, for på den måten å kunne skape en positiv sirkel av rehabilitering. I mer enn 20 år har Rehabilitation of Arid Environments Trust (RAE) undervist og veiledet bønder i Baringo området (Kenya) i rehabilitering og forvaltning av forvitret og skrin jord, ved hovedsakelig inngjerding og arrondering. Målet med denne studien var å kartlegge sekvestreringspotensialet og det eksisterende karboninnholdet i to av disse områdene. Det ble tatt parede prøver fra innsiden og utsiden av det elektriske gjerdet.
For å utfylle jordprøvene ble det gjennomført en grundig vegetasjonskartlegging samt flere infiltrasjons målinger og jordtetthetsprøver. Resultatet fra jordanalysene (Vis-NIR spektroskopi) viser ingen forskjell i det organiske karboninnholdet i de inngjerdede områdene sammenlignet med områdene rundt. Jordas organiske karboninnhold befinner seg i område 4.0-27.4 Mg K ha-1 (1.2-10.7 g K kg-1). Det er heller ingen forskjell i karboninnhold mellom de to rehabiliterte feltene. Begge de rehabiliterte områdene viser en høyere infiltrasjons kapasitet og en lavere jordtetthet, enn de omkringliggende arealene. Når det gjelder det organiske karboninnholdet, ser det ut til at de områdene med mye trær og busker, høy infiltrasjonskapasitet, lav jordtetthet og lavt sandinnhold, har det høyeste karbon sekvestreringspotensialet. Sekvestreringspotensialet i Baringo ligger i området 0.14-0.41 Mg K ha-1 år-1. I løpet av en periode på 20-50 år er det estimert en netto karbon tilførsel på til sammen 240-1765 Mg K (86 ha). Det viser seg at blant de ulike sekvestreringsstrategiene, er det hvor man i samarbeid med lokal samfunnet søker vinn-vinn løsninger, med økt levekår gjennom forbedrede økologiske forhold, de beste resultatene oppnås.
Table of content
Declaration ... I Acknowledgement...II Abstract ... III Sammendrag... IV
1.0 Introduction ... 1
2.0 Literature review ... 3
2.1 Carbon sequestration ... 3
2.2 Land degradation and ecological resilience ... 6
2.3 Methodological approach ... 8
3.0 Material and methods ... 9
3.1 Research design... 9
3.2 Study area ... 9
3.3 Fieldwork and sampling frame... 13
3.4 Data collection... 14
3.5 Chemical and physical analyses ... 16
3.6 Statistical analyses... 18
4.0 Results and discussion... 19
5.0 Conclusions ... 28
6.0 References ... 29
Appendix ... 32
1. LDSF field recording sheet ... 32
2. List of plant species found in the study area ... 33
1.0 Introduction
Background
Carbon sequestration has been emphasised by many scientists as a way to mitigate the increases in atmospheric CO2 (Batjes, 1999; Lal, 2000). In addition to reductions in anthropogenic emissions, carbon sequestrating may be effective in reducing atmospheric CO2, through relocation of carbon from the atmosphere to the biosphere (Ingram & Fernades, 2001). Restoring degraded land and ecosystems is by Lal, (2004a, b) highlighted as one of the most important ways of increasing sequestration of atmospheric CO2. The large sequestration potential is due to a relatively low carbon content in degraded land. Batjes (2004) suggests that 5-9% of Kenya’s CO2-C emissions from fossil fuels, industry and land use change can be sequestrated annually. Several studies present good results of carbon sequestration in different ecosystems (Batjes & Sombroek, 1997; Batjes, 2004), but because of the carbon cycle complexity, it is difficult to get a total overview. Uncertainties in measurements and methodology make the work even more difficult (Houghton, 2003). Especially the methods of collecting and analyzing data have differed widely, mainly with regard to time of year and auger depth, which leaves us unable to compare studies (Tiessen et al., 1998). Other problems are the logistical issue, accessibility and high measurement costs, which make it nearly impossible to monitor the scale of the problem (Vågen et al., 2005).
In the Baringo District, land degradation has become a critical problem during the last several decades, leading to famine in dry years, extreme flooding in wet years and general social tension or unrest over for instance grazing rights. The Rehabilitation of Arid Environments (RAE) Trust is a local non- governmental organisation dealing with the environmental degradation problems by focusing on overgrazing
in the Baringo District (RAE, 2007). For more than 20 years RAE has been teaching and training the local society to practice more organized and better managed grazing (Fig. 1).
FIGURE 1. Rehabilitated area in the Baringo District (Photo: R.
Stene).
Fencing and cultivation of areas, both as private and communal fields, have led to an improvement of the local environment and the farmers` income. According to RAE around 38 communal fields have been established since 1982, covering 1350 ha. In 1994 they started planting private fields, today counting about 680 fields ranging from 0.5 to 10 ha (RAE, 2007).
As a preparatory analysis for the United Nation Development Programme (UNDP) -project
“Land rehabilitation and carbon sequestration in the lowlands of Baringo District, Kenya”, ecological enhancement following rehabilitation is examined. Basic soil properties focusing mainly on sequestration of soil organic carbon (SOC), are studied to locate differences between rehabilitated and non-rehabilitated land. Using the Land Degradation Surveillance Framework (LDSF) developed at ICRAF (Appendix 1), this study attempts to assess sequestration rates in two fenced rehabilitated fields in the Baringo District. The hypothesis is that SOC levels will be higher in rehabilitated fields than outside, due to increased carbon sequestration in reclaimed fields. This study focuses on below-ground carbon (SOC). If good results are shown, the next step for the UNDP-project is to determine if a financial flow from the carbon market could support land rehabilitation activities in the Baringo District.
2.0 Literature review 2.1 Carbon sequestration
Due to the importance of SOC, many studies have focused on how to restore or increase SOC in ecosystems. In areas with land degradation, soil carbon sequestration might be achieved through rehabilitation, and according to Batjes (2004) the focus should be on the inputs and loss of SOC through soil organic matter. A combination of increased use of organic and inorganic fertilizers, cover corps, introduction of new cultivating methods, erosion control, water conservation and management, and introduction of new crop/plant species are, according to Bruce et al. (1999), concrete ways of improving soil organic matter status and increasing SOC. Feller & Beare (1997) emphasized that restoration of SOC levels most certainly will make the ecosystem more resilient to disturbance and degradation.
Although several studies have shown good results in carbon sequestration, uncertainties in measurements and methodologies, combined with the complexity of the carbon cycle make it difficult to get a complete overview (Houghton, 2003; Batjes, 2004). Differences in analytical methods and data collection (e.g. auger depth and time of year), make it even more difficult to compare studies (Tiessen et al., 1998). Vågen et al. (2005) also emphasize logistical issues, including accessibility and measurement cost, when using conventional methods, making accurate assessments very difficult.
Protected grasslands, enclosures and degraded land may sequester or lose carbon for 20–50 years before equilibrium is reached (Sampson & Scholes, 2000). The net gain of carbon typically diminishes over time, depending on soil properties and management (Table 1), and according to Silver et al. (2000) SOC is expected to accumulate faster the first 20 years than later in the organic matter development. Carbon is stored as biomass above- and below- ground and as soil organic matter (litter, macro aggregates, charcoal carbon). The different carbon pools have varying residence times, ranging from days to several centuries. The residence time of soil organic carbon depends on climate, management, level of disturbance and in which form the carbon is stored (Paul et al., 1997). There is generally a higher SOC stock under trees in savannah areas compared with open grassland areas (Belsky et al., 1993).
Watson et al. (2000) estimated that trees may sequester 0.2-3.1 Mg C ha-1yr-1. Savannahs converted to cultivated land may increase SOC, depending on management and soil texture.
In a review Vågen et al. (2005) estimated agroforestry (fallow systems) to sequester 0.1-5.30 Mg C ha-1 in former savannahs. Tiessen et al. (1998) also found a significantly difference in C input between dry- and rainy seasons, ranging from 0-15 Mg C ha-1yr-1 in a West African savannah system. Table 1 indicates that wet areas have a higher sequestration rate than dryer areas.
There are mainly two ways of increasing SOC, a change in land use or a change in management. Often these are used in combination. Change in land use may include various conservations, including for example cropland to grassland, grassland to agroforestry or cropland to forest. Management changes lead to higher SOC by focusing at practises that improve the current vegetations potential, including for example species introduction, reduced cultivation, fertilization in cropland, irrigation, plant protection and improved grazing management (Samson & Scholes, 2000).
TABLE 1. Summary of potential rates of carbon gain for different practices. The rate is average sequestration from the time a practice is introduced to carbon storage reaches equilibrium (Sampson & Scholes, 2000).
Activity Ecozone Key pracitces
Rate (Mg C
ha-1 yr-1) Duration# Agroforestry
management Tropical Improved management 0.5-1.8 25
Grassland management
Temperate - dry
Grazing management, fertilization, irrigation 0-0.3 50
Temperate - wet
Grazing management, species introduction,
fertilization 0.4-2.0 50
Tropical - dry Grazing management, species introduction, fire
management 0.1-1.5 40
Tropical - wet Species introduction, fertilization, grazing
management 0.2-3.9 40
Forestland management
Boreal and temperate - dry
Forest regeneraton, fertilization, plant density,
improved species, increased rotation length 0.1-0.8 80
Temperate - wet
Forest regeneration, fertilization, species change 0.1-0.3 50
Tropical - dry Forest conversation, reduced degradation 1.75 40
Tropical - wet Reduced degradation 3.1-4.6 40
Conversation to agroforestry
Tropics Conversation from cropland or grassland at
forest margins 1.0-5.0 25
Conversation (cropland to grassland)
Temperate - dry
Marginal cropland re-seeded to grassland 0.3-0.8 50
Temperate - wet
Surplus cropland seeded to grassland 0.5-1.0 50
Restoration of degraded land
All Restoration of eroded lands, saline soil
reclamation 0.1-7.0 30
# The expected time of sequestration
Soil type and soil depth are also variables that may have an impact on the potential amount and rates of carbon sequestration and storage (Sampson & Scholes, 2000). In a paper viewing carbon sequestrating at a global scale, Smith (2004) stated that the strategies that locate and come up with win-win situations, where SOC sequestration at the same time improves other aspects, such as environmental services and protection or increase of existing stocks (improved yields of agricultural or forestry products) are likely to be most successful. Choise of best management practices will also heavily depend on land use history, climate and soil conditions (Batjes, 2004).
2.2 Land degradation and ecological resilience
Land degradation resulting from population increase, deforestation, overgrazing and cultivation is on the increase in sub-Saharan Africa. The problem is particularly acute in subsistence farming systems, where extreme poverty and food shortages force local communities into utilizing marginal areas for grazing or cultivation. Human-induced climatic changes result in heavy rainfall, hot winters, increased number of storms and extreme drought. Land use change is one of the greatest and most obvious challenges mankind has ever encountered. Both at the global and local scale, human activity continually changes the landscape. While atmospheric emissions mostly have global effects, land use change also often results in degradation of ecosystems at a regional and/or local scale. Changes in land use have many faces, including for instance deforestation, expanding urban settlements and intensification in farmlands. The results of these changes are formidable, resulting in loss of biodiversity and overexploitation of native species, disturbed water- and energy balances and declines in water quality, to mention some examples (Pimm & Raven, 2000). In a review paper by Foley et al. (2005), changes in land use during the last decade, with the extensive degradation of ecosystem services, such as food, fibre, shelter and freshwater was highlighted as a major concern. This forms a paradox, where changes in land use to maintain and cope with the basic needs of increasing human populations undermine the opportunities to meet the same needs. A good example is food production, which all has increased both in area and amount. The world cropland has increased with around 12%, resulting in a grain harvest exceeding 2 billion tons a year (Mann, 1999). Extended use of fertilizers, pesticides and irrigation have also contributed to this development in food production. Results of this intensification of agriculture are contaminated groundwater, reduced water quality and salinization. Wood et al. (2000) estimated that almost 40% of the world’s arable land might suffer from erosion, overgrazing or reduced fertility.
Nature is not a balanced system filled with nice biogeochemical cycles, circulating in a predictable pattern, but instead consists of complex and adaptive systems, where extreme events, such as floods and droughts, form a natural part of the reality (Rockström, 2003).
Nature has its own way of dealing with randomness, shock and extreme events as Rockström describes above, often referred to as ecological resilience. Ecological resilience is what makes ecosystems able to “bounce back” after extreme events and maintain their functions (Holling,
1986). The human activities described above are changing the landscape and therefore also influencing the resilience of ecosystems. Vitousek et al. (1997) states that human beings have occupied almost every natural ecosystem on Earth, and that we are more or less reliant on the ecological life-support systems. Managing and building ecological resilience is therefore an important way of coping with human-induced land degradation. This is especially important in semi-arid areas, where the rural society is totally dependent on the local ecosystem’s resilience and production capacity. Another term often used in connection with ecological resilience, is social resilience. This is described as social systems, culture, laws and institutions that make the community able to handle disturbances and shocks (Rockstöm, 2003). The RAE Trust has taken this into consideration, by focusing both on the social and the environmental aspects in their rehabilitation work (RAE, 2007).
The Rehabilitation of Arid Environments Trust
The Rehabilitation of Arid Environments Trust (RAE) is a non-governmental organisation founded by Murray Roberts and Dr. Elisabeth Meyerhoff, with it’s headquarters located close to Lake Baringo at Kampi ya Samaki. For more than 20 years RAE has been teaching and training the local society and farmers in more sustainable farming. Due to the problems with overgrazing in the Lake Baringo area, the RAE has mainly focused on reclaiming arid land.
The RAE provides help on a cost share basis and on the farmers’ initiative. By teaching how to reclaim and restore degraded land, RAE helps the society improve their livelihoods in several ways. Under supervision of the RAE Trust, farmers have started fencing areas for better managed and controlled grazing, including both smaller private fields and larger communal fields (i.e. areas that are shared and managed by groups of farmers). This work has shown very good results. By using the reclaimed land as a buffer in dry periods, farmers are able to maintain sufficient livestock, which strengthens the community’s social resilience.
The farmers also collect grass seeds for sale, and some keep bees for honey production. The ecological benefits of these improvements are among others reduced erosion and soil degradation, and increased biodiversity of both flora and fauna (RAE, 2007).
2.3 Methodological approach
To meet the problems in measuring and monitoring SOC, a new method has recently been introduced based on the use of diffuse reflectance spectroscopy (DRS, also referred to as Vis- NIR spectroscopy). Other areas of science, such as the pharmaceutical and the food industry, have used this method for a long time. Vis-NIR spectroscopy represents a relatively simple, fast and cost effective analysis procedure (Shepherd and Walsh, 2002), which makes it easier for researchers, local authorities, farmers and land managers to predict and map soil condition (Vågen et al., 2006). By exposing each soil sample to a light source and measuring the absorbance, a fingerprint spectrum of every sample is made. A small selection (normally 15- 20 %) of samples are also analysed by standard wet chemical methods, and used to calibrate prediction models. The soil spectra are pre-treated with different multivariate calibration techniques. Pre-treatments include calculating first-derivatives to reduce effects of grinding and optical setup, using PCA (principal component analysis) and MSC (multiplicative scatter correction). PCA is way of decomposing the spectra into fewer components, and MSC reduces multiplicative scattering effects. Finally, PLSR (partial last square regression), a multivariate regression technique, is used to develop the prediction models, which predict the properties of the remaining soil samples.
Visible-near-infrared-reflectance spectroscopy (Vis-NIR) is a technique where the interactions between incident light and a material’s surface, chemistry and physical structure, expressed by specific vibrations and overtones for different organic functional groups are used for detection (Chang et al., 2001). This is a rapid technique that needs little sample preparations. The Vis-NIR spectrum is strongly affected by soil properties as total carbon and nitrogen, water, texture, particle size, moisture content and surface properties. According to Chang et al. (2001) soils from the same area usually have the same composite and similar soil properties (soil particle-size, aggregates, colour, organic matter, texture, soil structure), and thus the same response to incident light. By selecting samples with similar spectra, the physical variations and local noise could be minimized in the prediction model. Chang et al.
(2001) emphasise that carbon and nitrogen are major substances in organic matter, generally have a similar spectral response, a high degree of correlation, and affect the accuracy and values of other biochemical properties.
3.0 Material and methods 3.1 Research design
This study was conducted as a part of the preparatory phase of a planned United Nation Development Programme (UNDP) project in the Lake Baringo area. The World Agroforestry Centre (ICRAF) and RAE Trust are conducting research and intervention activities as part of the preparatory phase. Two fields were chosen for the study, one in upland areas west of Lake Baringo (Field V) and one in the lowland areas by the lake (Field I). Soil samples were collected using a paired sampling approach, from within and outside the fenced fields. The fieldwork was done during period 28th of January to 12th of February 2007.
3.2 Study area
The Baringo District
The Baringo District is located in the eastern part of the Rift Valley province (Fig. 2). The district covers around 11.000 km2 with nearly 200 000 inhabitants. As much as 70 % of the district is semi-arid land, partly unproductive, and in many areas subject to severe soil erosion and loss of vegetation. The Baringo District has been a subject of concern for both the Kenyan government and international NGO’s for a long time. Already in 1974 the Baringo area was categorized as an “ecological emergency area”. Twenty years later the situation is nearly status quo, but recently the government
and international organisations FIGURE 2. Satellite picture of the Lake Baringo area (Vågen, 2007).
The rehabilitated fields are marked by GPS records done in situ (Stene, 2007).
are beginning to face the massive problems present in the region. Land degradation, desertification and the drying up of Lake Baringo are the most obvious and urgent problems that need attention and action (RAE, 2007). Deforestation rates in the Lake Baringo basin are formidable. During 25 years (1976 to 2001), the natural forest-cover decreased by almost 50%, from 829 km2 to 417 km2. Odada et al. (2006) state that the same amount of ecological benefits and services provided by the forest may be lost.
Apart from some industrial development near the lake, agriculture and pastroalism are still the main livelihoods in the Baringo District. There has been a dramatic increase in agricultural intensification and livestock densities, leading to more intensive grazing, both in lowland and upland areas. The farmers living in Baringo are mostly semi-pastoralist, and the grazing areas are mainly communal. Tribes present are Tugen, Pokot and Ilchamus. An increased number of farmers and larger livestock herds have led to overgrazing in communal grazing areas, and in combination with increased need of timber, fuel wood this has resulted in many areas becoming severely degraded and currently unproductive. The environmental degradation leads to erosion and in some places desertification (Odada et al., 2006). Less arable land gives fewer opportunities for generating income. Heavy communal grazing combined with a drought period would reduce the potential income and lead to increased poverty. In combination with lack of land policies, ethnic conflicts, insecurity and skills to cope with the changed reality this could lead to a breakdown of social and cultural norms that are crucial for the local civilization (RAE, 2007).
Lowland field (Field I)
Field I is located at the Njemps flats southwest of Lake Baringo (Fig. 3).
The field is located between 063900 N – 064800 N and 167500 E – 168400 E (UTM zone 37N), at 990 m.a.s.l. (Fig. 2). The topography is classified as level (1-2o slope) bottomland using the FAO Land Cover Classification System (LCCS). Field I is made up of three
FIGURE 3. Heavily grazed and degraded land at the Njemps flats, close to Filed I. Vegetation is Acacia tortilis. (Photo: R.
Stene)
different, and separate, fenced fields (F 1-1, F 1-2 and F 1-3), established in the period from 1982 to1987, in communal areas. Field I is about 33 ha in total, and managed by the Naitemu Women’s Group. Main activity is livestock fattening, honey production and grass seed harvesting for sale. The vegetation in this area is classified as Acacia tortilis savannah woodland (Kiyiapi, 1994), and according to Snelder & Bryan (1995) the area has been used for grazing since the early 1900s. The fields were first planted with trees, but later grass has been planted due to its importance to the local framers. If the grass cover becomes scare reseeding (Cenchrus ciliaris) is conducted.
Upland site field (Field V)
Field V (F 5) is located in the lower Tugen Hills (Fig 2.), west of Lake Baringo, between 070300 N - 071400 N and 832000 E - 833000 E (UTM zone 36N), at average altitude of 1130 m.a.s.l (Fig. 4). The total area of this field is approximately 53 ha. The major landform is level (1-2o slope). Local farmers under supervision of RAE established the field in 1985.
Today Field V is managed as a communal field. Initially, both indigenous and exotic trees
were planted, but later also grass planting was carried out. Field V has been reseeded twice.
Cemchrus cilliaris was the main grass species planted, and later grass species such as cymbopogon pospiuchilii, sehima nervosum and aristida spp. have regenerated.
Approximately 30-50 households benefit annually from Field V. Yearly, the field provides several benefits for the local community, such as grass seeds harvesting for sale, dry season grazing and fattening, and thatched grass cutting for sale and home use. The environmental
FIGURE 4. Left: Seven years old private field close to Field V, fenced with cactus and shrubs.
Right: Electric fencing powered by solar panel (centre of picture) in Field V. (Photo: R. Stene)
benefits such as reduced soil erosion, improved soil fertility and increased biodiversity are also highly observable, but not yet quantified.
Climate and land cover
According to Sombroeks et al. (1982), the Baringo District is in agro-climatic zone (ACZ) V, or semi-arid. Semi-arid areas have medium to low potential for crop production, and a 25- 75% risk of crop failure. According to Sombroek et al. (1982), the major limitations for crop production are rainfall, soil fertility and drainage. The typical land-cover in semi-arid areas is bush land with cropland and natural vegetation mosaic (31%), woody savannah (17%), closed shrub land (14%), savannah (13%) and cropland (11%) as the major land-cover classes.
The area is predominantly a low tree and shrub savannah, with a tree height around 3 meters and tree density less than 1000 trees ha-1 (Etien, 1972). The rainfall distribution is bimodal (Fig. 5), with two rainy seasons, from April to August and October to November. The mean annual rainfall is 650 mm, and varies between 450 and 900 mm, with an annual potential evapotranspiration of 1650-2300 mm (Odada et al., 2006).
Soil
The main soil type around the lake is saline Solonchaks with Cambisols and Lithosols on slopes in upland areas and small patches of Fluvisolos (FAO, 2004). Dominant texture classes are silt loam and silty clay loam (Snelder & Bryan, 1995).
FIGURE 5. Monthly variability and distribution in rainfall (mm), (1951-2000) in the Baringo district (DWD, 2007).
The figure is presented with min, Q1, median Q3, max and outliers.
3.3 Fieldwork and sampling frame
Soil and vegetation sampling procedures were conducted using the Land Degradation Surveillance Framework (LDSF) (Walsh & Vågen, 2006) sampling design. The upland and lowland communal rehabilitation fields were selected after a reconnaissance survey with the administrator of the RAE Trust and ICRAF. Around both fields, 21 pairs were sampled within and outside the rehabilitated areas at randomized distance form the fence lines. Each sampling plot consisted of 4 sub-plots at the plot centre and on radial arms 12.2 meters from the centre- plot (C) (Fig. 6). Within sub-plot, covering an area of 0.01 ha (100 m2), information about landform, soil surface characteristics, visible erosion, and vegetation types and structure were recorded or measured. Field texture was also assessed, in addition to soil sample collection.
The size of each plot was 0.1 ha (1000 m2) area. Infiltration tests were conducted plot centre- points.
FIGURE 6. The sampling plot forms a 120-degree Y. Sub-plot 1, facing upward the slope direction (Walsh &
Vågen, 2006).
3.4 Data collection
Soil surface characterization
Topsoil (0-20 cm) and subsoil (20-50 cm) samples from the four subplots were collected separately in small buckets (Fig. 7). The four topsoil samples were mixed in a lager bucket, and a 500 g sample was pooled into a small paper bag. The same procedure was followed for subsoil samples. The paper bag was labelled
with field and plot ID. If ground conditions restricted augering at any sampling location, the depth of restriction was recorded to the nearest 5 cm. In addition to soil sampling, a visual assessment was made of soil characteristics describing visible signs of erosion and land use, and this information was recorded. Erosion types were noted as none, sheet, rill or/and gully/mass. From the centre-plot of each Y (Fig.
6), a bulk density sample was collected, placed in a small paper bag and labelled. Soil texture was also determined and noted (Walsh &
Vågen, 2006).
Soil infiltration capacity
The duration of each infiltration measurement was set to 135 minutes (5 min x 6, 10 min x 6 and 15 min x 3). A single ring was used in the infiltration measurements, which were conducted near the centre-plot, after carefully removing stones and (Fig. 8). Due to time limitations infiltration capacity measurements were conducted at 12 of the 21 sampling pairs in each field. Where there was leakage due to termite-holes or rocky soils, the rings were moved slightly and the measurements restarted.
FIGURE 8. Water infiltration measurement setup (Photo: R.
Stene)
FIGURE 7. A precise and robust framework for the field sampling and measurements is decisive. Picture: soil sampling inside Field V (Photo: R. Stene)
Land form and land cover classification
Landform and topographic position was assessed visually and classified into a suitable category (Annex 1). The land cover type was recorded using the FAO Land Cover
Classification System (LCCS), which has 8 different land cover types. Dominant vegetation life form (tree, shrub, herbaceous), cover, leaf phenology and morphology, and spatial and floristic aspects were noted, first for the whole plot and then for the sub-plot level.
Woody vegetation measurements
Woody vegetation was measured in every sub-plot by the T-square method (Krebs, 1989), which is a distance-based measurements procedure for determining vegetation distribution (i.e. uniform/non-uniform and random/non-random) (Fig. 9). The point-to-plant distance (x) was recorded, followed by plant-to-plant distance (t). For the second plant, biovolumes (width x length x height) were measured for shrubs, and diameter in breast height (DBH) and height were measured and recorded for trees.
FIGURE 9. The T-square sampling method. x showing the point-to-plant distance, and t the plant-to-plant nearest distance, which was found in front of a line 90 degree on the x distance (Walsh & Vågen, 2006).
3.5 Chemical and physical analyses
The soil samples were analysed at ICRAF`s headquarters in Nairobi, Kenya. After air-drying in paper bags for 36 hours, all samples were sieved to pass a 2 mm mesh screen. The samples were listed and loaded to Petri dishes and finally analysed by visible-near-infrared reflectance spectroscopy (Vis-NIR).
Spectral analysis
The soil samples were analysed with a standard procedure using a Fourier-transforming Vis-NIR spectrometer (Sherman, 1997) (Fig. 10). Hardware provided by a Bruker Multi Purpose Analyser (MPATM) (Bruker, 2006a), and software from OPUS Lab (Bruker, 2006b). An ICRAF Mua Hills soil standard and a kaolinite standard were used to calibrate the spectrometer.
Prediction models
After wet chemistry analysis of 36 samples (about 20% of the total number of samples), prediction models were developed using partial last squares regression (PLSR) (the SIMPLS algorithm) on the first derivatives of the spectral data. PLSR is a method for relating a data matrix to another data matrix, via bi-linear low-rank regression modelling (Dijksterhuis et al., 2005). The first derivatives were pre-treated by principal component analysis (PCA), which decomposes the spectra into fewer components, and multiplicative scatter correction (MSC) which reduces noise as multiplicative and additive scatter effects, and emphasises the chemical composition of the sample to get a higher degree of linearity in the model. All model-fitting was done in R-statistic (www. R-project.org) using the pls package (Wehrens &
Mevik, 2006). Vis-NIR spectrum analyses and prediction modelling (PCA, MSC and PLSR) were conducted by Dr. Tor-G. Vågen (ICRAF).
FIGURE 10. Bruker Multi Purpose Analyser (MPA). Petri dishes with pre-treated soil samples (left). (Photo: R. Stene)
For the principal component analysis (PCA) the first 10 PCs usually explain 97 to 100% of the variation (mean 99.5%), which makes it adequate to use less PC (Fig. 11b) as independent variables in NIR-PCA (Chang et al., 2001). The PCA plot presents the location of every sample along the first two PCs. The clustering in Figure 11b shows the same pattern as in Figure 11a, due to differences in soil properties (total carbon and nitrogen, water, texture, particle size, moisture content, colour and aggregation). The ellipse up right presents the upland field, with a higher degree of variance than the lowland field. The samples tend to separate along both the PC axes (Fig. 11b).
The spectrum curves (Fig. 11a) show tree major peaks. The responses at 1400, 1900 and 2200 nm are primarily due to absorbance of O-H bonds (Al-OH and Si-OH), especially hygroscopially bound water, but also O-H groups bound in clay lattice and different oxides, such as Vågen et al. (2006) found in a study of Oxisol in Madagascar.
Differences between the two average spectra (red and black line) in Figure 11a, are due to divergences in texture, moisture, aggregation, total carbon and nitrogen, water and particle size. To test the prediction model, the predicted values were compared to measured (wet
FIGURE 11. a) Mean absorbance with + / - standard deviation for Field I (black line) and Field V (red/broken line).
b) Distribution of the first two principal components (PC1 and PC2) for the raw spectre data (Vågen, 2007).
b) a)
chemistry) values in a correlation test (Fig. 12). Compared with results form Chang et al.
(2001), the model used give quite similar correlation values, with an acceptable accuracy. The prediction gave a correlation for SOC and total N at R2 = 0.92 and R2 = 0.91, respectively (Fig. 12).
3.6 Statistical analyses
Statistical analyses were performed in R, version 2.4.0 (R-project, 2003). Analyses of variance tests (ANOVA) were used to determine differences between rehabilitated and non- rehabilitated areas. A p = 0.05 significance level was used to test the hypothesis. The
correlation models were calculated in Excel (2003 version). Infiltration rates were estimated using a nonlinear mixed-effects model (NLME) in R.
Sand (%) R2 = 0.9006
0 20 40 60
0 20 40 60
Me asured value s
Vis-NIR values
FIGURE 12. Relationship between some basic soil properties measured by standard laboratory procedures and predicted by Vis-NIR spectroscopy technique (n = 36). Lines shown are 1:1 relationship.
BS (%) R2 = 0.9453
0 20 40 60 80
0 20 40 60 80
M e as ur e d value s
Vis-NIR values
Tot N (g kg-1) R2 = 0.9071
0 0.2 0.4 0.6 0.8 1 1.2
0 0.2 0.4 0.6 0.8 1 1.2
Measured values
Vis-NIR values
SOC (g kg-1) R2 = 0.9225
0 2 4 6 8 10
0 2 4 6 8 10
Measured values
Vis-NIR values
4.0 Results and discussion
Carbon stock
The reclaimed fields have a higher vegetation cover than non-rehabilitated land, with more plant species present in Field V than in Field I (Appendix 2). Because of recent rains, almost all the plants were green with healthy leaves at the time of field work, some few in flower (i.e.
Acacia nilotica and Acacia aneura are in yellow flowers) and the grass species in green seed.
According to local farmers, most of the plant species found outside the two fields are plants not favoured by livestock as fodder. Biodiversity is visually relatively higher in the reclaimed areas, with several bird, insects and reptile species observed.
The soil organic carbon stock in the Baringo District is below the average compared with other semi-arid areas, ranging from 4.0 to 27.4 Mg C ha-1 (1.2-10.7 g C kg-1) (Table 2). This are relatively low concentrations compared to what Batjes (2004) reported (36-37 Mg C ha-1) for Agro Climatic Zone V (ACZ) (the Baringo District) in a study of Kenyan soils. In other East African savannah systems (Tanzania), Solomon et al. (2000) measured SOC content to 18.7 g C kg-1. For bush savannah systems in Senegal under different levels of cropping Manlay et al. (2004) reported SOC stocks in the range 11.7-21.3 Mg C ha-1.
On average, there is no significant difference in SOC between upland and lowland fields (Table 3). There is, however, significant variability in SOC content between the three lowland sites (in Field I) (Fig. 13), mainly due to differences in sand content (Table 3), but the differences in SOC between the three sites in Field I may also be partly explained by differences in woody cover (trees and shrubs) (Fig. 16). Concentrations of SOC are similar in top- and subsoils (Fig. 13) in all sites.
TABLE 2. SOC stock (Mg C ha-1) in topsoil (0-20) and average SOC stock (g kg -1) in top- and subsoil (20-50) (n = 161).
Location Median Min Max Median Min Max
Treatment Mg C ha-1 g kg-1
Upland Rehabilitated 14.6 5.9 22.2 4.9 1.6 9.5
None 11.4 4.7 27.4 4.1 1.5 10
Lowland Rehabilitated 15.6 4.4 22.9 4.5 1.2 10.7
None 6.4 4.0 18.1 3.8 1.7 10.4
The insignificant difference in SOC between the rehabilitated and non-rehabilitated (Table 2) areas could be a result of shrub diffusion. Shrub density is significantly higher in Field V than in Field I. In a shrub count, the upland field had, on average, 198 shrubs per hectare than the lowland field. The rehabilitated areas had on average 19 shrubs per hectare less than the non- reclaimed land, probably because of the upland condition. If carbon sequestration is to be the main purpose of the rehabilitation, a different vegetation composition with more woody species could be a strategy to gain more SOC.
TABLE 3. Mean and standard error (SE) from prediction of 161 samples. Different letters between columns shows significant difference (p < 0.05).
Soil property ——— Lowland —— ——— Upland ———
Degraded (n = 42) Rehabilitated (n = 41) Degraded (n = 40) Rehabilitated (n = 38)
Mean SE Mean SE Mean SE Mean SE
pH 8.6 a 0.02 8.63 a 0.03 6.73 b 0.04 6.8 b 0.05
SOC (g kg-1) 4.86 0.40 4.94 0.42 4.54 0.35 5.09 0.29
TN (g kg-1) 0.50 0.04 0.51 0.04 0.44 0.03 0.50 0.03
K (g kg-1) 0.84 a 0.04 0.86 a 0.04 1.26 b 0.04 1.21 b 0.04
Ca (g kg-1) 38.93 a 0.89 42.41 a 1.32 10.79 b 0.43 11.38 b 0.35
Mg (g kg-1) 8.63 a 0.17 8.96 a 0.20 4.33 b 0.09 4.35 b 0.09
BS (%)* 48.5 a 1.19 51.3 a 1.43 16.4 b 0.45 17.1 b 0.40
P (mg kg-1) 7.30 a 0.51 7.42 a 0.61 2.75 b 0.18 3.20 b 0.19
Sand (%) 27.05 2.57 26.20 3.24 26.94 1.65 28.60 1.68
* Base saturation
FIGURE 13. a) SOC content in the four sites (n = 161). b) SOC distribution in topsoil (0-20 cm) and subsoil (20-50 cm) n = 161. Figures presented with min, Q1, median, Q3, max and outliers.
a) b)
Sequestration potential
The sequestration rate in Baringo is estimated to be 0.14 Mg C ha-1 yr-1 for upland and 0.41 Mg C ha-1 yr-1 for the lowland, which is in the upper range of what Bajes (2004) reported from ACZ V (Table 4), and within what Sampson & Scholes (2000) stated in the IPCC-report.
What makes this estimate uncertain is the fact that the value derived of an insignificant difference in SOC, based on the median Mg C ha -1 inside and outside (Table 2), divided by the age of the field. Since there is no reference value existing from the time before the areas were fenced, the non-rehabilitated area function as reference (control). According to local farmers the rehabilitated areas were in the same condition as the non-reclaimed land is today, before they were fenced. The different sequestration rates between Fields I and V may be due to relatively high shrub densities outside Field V (Fig. 14e), which leads to similar sequestration rates outside the rehabilitated areas as within Field V.
In a special report of the Intergovernmental Panel on Climate Change (IPCC), Sampson &
Scholes (2000) present several strategies and fact sheets on carbon sequestration, where grassland management in dry tropical environments, including grazing management, species introduction and fire management, have predicted sequestration rates of 0.1-1.5 Mg C ha-1yr-1 (Table 1). Associated impacts will (potentially) be reduced soil degradation, higher productivity and woody encroachment. The RAE fields (I and V) in Baringo could also be compared with what the IPCC–report calls protected lands and set-asides (Table 1), where sequestration rates are found to be in the range 0.2-1.3 Mg C ha-1yr -1 with an improvement in biodiversity as an external impact, especially in the set-asides. Another comparable activity called restoration of degraded land has a potential ranging from 0.1 to 7.0 Mg C ha-1 yr-1 (Table 1). Vågen et al. (2006) found carbon sequestration values ranging from 0.1 to 5.3 Mg
TABLE 4. Indicative carbon sequestration rate (Mg C ha-1yr-1) in different agro-climatic zones in Kenya (ACZ) (Batjes, 2004).
ACZ C sequestration rate
I Humid II Subhumid
0.30-0.50 0.30-0.50 III Semihumid
IV Semihumid-semiarid
0.15-0.30 0.15-0.30 V Semiarid
VI Arid
0.05-0.15 0.05-0.15
VII Very arid 0.00-0.05
Based on data form Bruce et al., 1999; Sampson & Scholes, 2000 and Lal, 2002.
C ha-1yr-1 in a West Sudanese savannah with managed agroforestry. The Baringo area is in the lower part of each of these estimates, and compared with other ecosystems and practices (Table 1), the sequestration rates in Baringo are relatively low. Still carbon sequestration in degraded areas is interesting, due to a low initial carbon stock and a relatively high sequestration potential before equilibrium is reached. The potential is, however, often limited by other factors as water and nutrient availability. In some cases fertilization could almost double the sequestration rate in areas like this (Sampson & Scholes, 2000).
Using the estimated sequestration rate, 0.14-0.41 Mg C ha -1yr -1, the potential SOC gain for the two fields are predicted to be around 12-35 Mg C yr-1 totally (86 ha). Considering the degree of degradation, chosen sequestration strategy and previous land use, the two fields together could sequester 240-1765 Mg C in a period of 20-50 years under proper management. To get a more precise estimate of the sequestration rate, new studies need to be conducted at regular time intervals. Sampson & Scholes (2000) suggest an interval of 3-5 years with sampling to a depth at least 1 meter.
Infiltration rate
The infiltration rate is perhaps, according to Wakindiki & Ben-Hur (2002), the most important process in the soil phase of the hydrologic cycle, affecting both the water supply to the soil and water-driven erosion. The infiltration rate is determined by bulk density, pore size, hydraulic conductivity and underlying soil. The general impression is that the infiltration capacity differs between the two fields, but there is also a high degree of variability within each field. The initial infiltration rate (sorptivity) is significantly higher in Field I compared to Field V (Fig. 14), while the saturated hydraulic conductivity (Ksat) is similar in the two fields (Table 5).
TABLE 5. Estimated saturated hydraulic conductivity in rehabilitated vs. non-rehabilitated areas (n = 42).
Field
F1-1 F1-2 F1-3 F5
Rehabilitated 132.6 105.5 122.5 93.7
None 95.7 68.5 85.6 56.7
0 50 100 150 200 250 300
0 20 40 60 80 100 120
Time (minutes)
Infiltration rate (mm hr-1)
Lowland Upland
0 50 100 150 200 250 300
0 20 40 60 80 100 120
Time (minutes)
Infiltration rate (mm hr-1)
Outside Inside
FIGURE 14. a) Average infiltration curves for upland and lowland sites. b) Average infiltration curves for rehabilitated (inside) areas versus areas that have not received any treatment.
a) b)
The differences in initial infiltration rates between uplands and lowlands can partly be explained by the higher bulk density in upland soils (Table 6). There is significantly higher sorptivity and Ksat (p = 0.09) in rehabilitated areas (Table 5).
In Field I, topsoil texture was generally sandy-loam, giving a relatively high infiltration rate.
Compared with Lal`s (1996) study the infiltration rates in Baringo are normal and as expected. In other studies infiltration capacity is shown to correlate strongly with soil bulk density, where a high density results in a low infiltration. Lal (1996) found that deforestation and cultivation increased soil bulk density, with infiltration capacity decreasing by as much as 20-30 % within a short time after deforestation. Lal (1996) also reported that sand content increased during cultivation after deforestation, probably due to erosion and eluviations that may lead to removal of clay and humus fractions. In this study, the sand has a relatively strong influence on SOC (R2 = -0.65) (Table 7). Deforestation is not the main issue in this study, but it clearly shows the importance and influence of woody vegetation cover regarding bulk density, infiltration capacity and SOC (Fig.15).
TABLE 6. Summary bulk densities n = 81.
Measurement Lowland Upland
Treatment Median Min Max Median Min Max
Bulk density (g cm3) Rehabilitated 1.14 0.97 1.51 1.23 1.09 1.64
None 1.15 0.98 1.54 1.27 1.03 1.45
TABLE 7. Correlation coefficients between different soil properties (n = 161).
Soil property pH SOC TN K Ca Mg BS P SOC (g kg-1) -0.089
TN (g kg-1) -0.048 0.991
K (g kg-1) -0.721 0.447 0.436
Ca (g kg-1) 0.915 0.211 0.251 -0.511
Mg (g kg-1) 0.901 0.228 0.267 -0.504 0.985
BS (%)* 0.908 0.244 0.282 -0.484 0.994 0.993
P (mg kg-1) 0.556 0.452 0.503 -0.051 0.665 0.656 0.681
Sand (%) 0.076 -0.808 -0.793 -0.484 -0.243 -0.287 -0.290 -0.423
* Base saturation
a) b)
c) d)
e) f)
FIGURE 15. Soil and vegetation properties in the four sites (F5 = Upland field; F1-1, F1-2 and F1-3 = Lowland field). The SOC and sand figures have sample size n = 161, bulk density n = 81, infiltration rate n = 42 and vegetation n = 336. Figure
Vegetation
Comparing Figure 15 with the satellite photo from Field I (Fig. 16) may give a picture of the correlation between SOC and woody vegetation. This fact is also known from literature. In a study by Bernhard-Reversat (1982) good correlation (R2 = 0.62) between total soil C and tree circumference was found. According to Belsky et al. (1993) there is generally a higher SOC stock under trees in savannah areas compared with grass areas. Reasons could be the shading from the canopy, nutrient enrichment from the litter inputs, nitrogen-fixing species, as well as differences in abiotic conditions (temperature, water, pH, mineral nutrients). In a review paper, Scholes and Archer (1997) show tree mechanisms that may contribute to soil nutrient enrichment beneath trees and other woody vegetation. Trees may function as nutrient pumps, soaking up and relocating nutrients from the sub layers to the upper horizon. The second mechanism is related to the fact that woody vegetation canopy may catch atmospheric dust containing nutrients that will be washed of the leaves and into the ground. The last mechanism is related to where birds and mammals seek food, shadow and cover adding manure to the nutrient cycle.
FIGURE 16. Satellite picture of Field I (left). The different colours indicate bare soil (red), perennial grassland (orange) and woody grasslands (green). Field 1-1 is located southeast, F 1-2 northeast and F 1-3 west. In Field V (right) the vegetation is dominated by perennial grass (orange). Dots around the fields mark the paired plots. The picture is analysed with Spectral Angle Mapper (SAM), (ASTER, 2004; Vågen, 2007).
Controlled and managed grazing, combined with improved local training and knowledge, result in enhanced livelihoods and environmental services. There is, however no reason for rehabilitating degraded areas if the cause of degradation is not tackled. Communal grazing areas need to be controlled and improved as the two RAE fields examined in this study. At the same time all the benefits of a sequestration program need to be made visible and implemented in the local society, or as Smith (2004) puts it; “the key to increased soil carbon sequestration, as a part of wider programmes to enhance sustainability, is to maximize the numbers of winners and minimize the number of losers”.
5.0 Conclusions
The potential for carbon sequestration in rehabilitating degraded areas in the Baringo District is estimated to be around 0.14-0.41 Mg C ha-1 yr-1, with a net SOC gain in a period of 20-50 years ranging from 240 to 1765 Mg C, which is relatively low compared with other studies.
However, this current study was only based on data from two sites and an expansion of the area sampled will be necessary to determine these potential more accurately. The highest carbon sequestration potential tends to be in woody vegetated areas, where shrub density is high, but there is not a significantly higher SOC level inside the fenced fields compared to the outside.
Although there is a week trend in increased SOC in the rehabilitated areas, there is no significant difference in SOC between upland and lowland fields. The rehabilitated areas however show a significantly higher infiltration capacity.
