Comparison and Prediction of the above Ground Carbon Storage in Croplands on the Inhabited Slopes on Mount Kilimanjaro (Tanzania) and the Taita Hills (Kenya)

Mount Kilimanjaro and the Taita Hills are adjacent montane areas that experience similar climate and agricultural activity, but which differ in their geologic history, nature of elevation gradients and cultures. We assessed differences in cropland above ground carbon (AGC) between the two sites and against environmental variables. One hectare sampling plots were randomly distributed along elevational gradients stratified by cropland type; AGC was derived from all trees with diameter ≥ 10 cm at breast height in each plot. Predictor variables were physical and edaphic variables and human population. A generalized linear model was used for predicting AGC with AIC used for ranking models. AGC was spatially upscaled in 2 km buffer and visually compared. Kilimanjaro has higher AGC in cropped and agroforestry areas than the Taita Hills, but only significant difference in AGC variation in agroforestry areas (F = 9.36, p = 0.03). AGC in cropped land and agroforestry in Kilimanjaro has significant difference on mean (t = 4.62, p = 0.001) and variation (F = 17.41, p = 0.007). In the Taita Hills, significant difference is observed only on the mean AGC (t = 4.86, p = 0.001). Common tree species that contribute the most to AGC in Kilimanjaro are Albizia gummifera and Persea americana, and in the Taita Hills Grevillea robusta and Mangifera indica. Significant and univariate predictors of AGC in Mount Kilimanjaro are pH (R = 0.80, p = 0.00) and EVI (R = 0.68, p = 0.00). On Mount Kilimanjaro, the top multivariate model contained SOC, CEC, pH and BLD (R = 0.90, p = 0.00), How to cite this paper: Dickens, O., Faith, K., Geoffrey, M., Petri, P. and Rob, M. (2018) Comparison and Prediction of the above Ground Carbon Storage in Croplands on the Inhabited Slopes on Mount Kilimanjaro (Tanzania) and the Taita Hills (Kenya). Journal of Geographic Information System, 10, 415-438. https://doi.org/10.4236/jgis.2018.104022 Received: May 19, 2018 Accepted: August 14, 2018 Published: August 17, 2018 Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Cropland is a broad land-use category and can be divided into three types based on the land management: rice paddies, cropped land and agroforestry systems [1]. The latter is characterised by crops and/or livestock combined with shrubs and trees, but with the density of trees falling below the threshold used for the Intergovernmental Panel on Climate Change (IPCC) Forest Land category [2].
The amount of carbon lost when forest or woodland is converted to cropped land varies considerably between the agroforestry and cropped land systems. About 40 -180 Mg C ha −1 aboveground carbon and 10 Mg C ha −1 topsoil are lost when forest is converted agroforestry, in comparison to an estimated 80 -400 Mg C ha −1 and 25 Mg C ha −1 respectively under conversion to continuous cropping [2]. Areas converted into agroforestry can be more productive than treeless alternatives and continue to provide environmental benefits for land users at all levels [3] [4].
Croplands in montane areas are part of forest or woodland that was converted by expanding agricultural communities. The conversion of forest, woodland or shrubland into cropland adversely affects the productivity of vegetation [5] and this is associated with the tree species and amount of carbon loss on the landscape. Some indigenous trees are often left and exotic trees are planted for food production and timber on cropland [6] [7] [8]. These areas, however, form important part of an agro-ecosystem that supports biodiversity and carbon sequestration [9].
The rate of cropland expansion in the horn of Africa has in the recent years decreased from 2.3% between the period of 1975 and 2000 [10] to 1.4% between 1990 and 2010 [11] [7]. Cropland cover area in East Africa has shown variation in estimation ranging from 1.8% [12] in some areas, 12.5% [13] and 22.7% in other areas according to Global Land Cover 2000 [14] in the late 1990s/early 2000s [15]. Around Mount Kilimanjaro croplands constitute over 50% of the land [16] and this is projected to cover 60% of the area by 2030 [17]. The lowlands and foothills of mountains experience expansion of croplands at the expense of thickets and shrublands [18]. The expansion of croplands is associated Journal of Geographic Information System with high human population growth [19] that exerts pressure on land due to increased demand for food.
Croplands in tropical Africa store about 5.3 Mg C ha −1 and the mosaic forest/cropland has 91.5 Mg C ha −1 [20]. In East Africa, the median range for carbon storage in cropland is estimated between 1.6 -4.8 Mg C ha −1 [21]. Existing literature that covers carbon standing stock and carbon soil in croplands [22] depends on large spatial scale studies that do not provide comprehensive and reliable information on local carbon storage in the sub-montane and montane areas in East Africa. Moreover, carbon storage in montane forests has been given more attention than the adjacent cropland. Carbon storage in the croplands of the Taita Hills occurs between 2.3 to 9.1 Mg C ha −1 [7]. In Kilimanjaro (Mwanga area) areas with high aboveground carbon storage occur in agroforestry areas which has an estimated average amount of 19.4 Mg C ha −1 (between 10.7 to 57.1 Mg C ha −1 ) [23].
Most studies on carbon storage are based on a particular biome (woodland and forests) and rarely look at cropland along an elevation gradient in mountainous areas. Elevation gradients play an important role in influencing climate; for instance, the highlands influences rainfall and temperature regime [24] that ultimately influences the distribution of vegetation [25]. Montane areas with shorter elevation gradients are characterized by compressed vegetation zonation and appearance of cloud montane forests in lower elevations [26] [27]. Soil texture discontinuity and micro-climate variation are influenced by short elevation gradients than longer gradients [27]. Besides this, the human population plays important role in determining the distribution of plant species and carbon storage [19]. A recent study by Adhikari et al. [28] showed that carbon stocks are highest on steep eastern and southern slopes of the Taita Hills, which are too laborious for agricultural practices, and thus remained in natural condition.
Kilimanjaro and the Taita Hills are located 100 km apart with similar climate characteristics although they have a very different geology; the Taita Hills are ancient crystalline mountains and Kilimanjaro is a more recent volcanic mountain. This geological difference impacts on the nature of soils and biodiversity in the areas [29]. The Taita Hills are the northern-most part of the Eastern Arc Mountain chain that is characterized by significant numbers of endemic species of plants and animals [29]. Both mountain areas are characterized by high human population growth and expanding small-scale agriculture which potentially affects carbon storage in croplands on the inhabited slopes. With the current and growing demand for balancing carbon storage, sustainable livelihoods and conservation of biodiversity, there is need to increase aboveground carbon sequestration in croplands [2].
The aim of this study was to assess the aboveground carbon and how its distribution is influenced by environmental variables on croplands on the inhabited slopes of Mount Kilimanjaro and the Taita Hills. The specific research questions were: 1) is there a difference in carbon storage between sites and different types Journal of Geographic Information System the crop-suitability of soils and climate: Lower Highland (LH)-maize, peas, potatoes, cabbages, cauliflower, kales, carrots, beetroot, spinach, lettuce, plums, passion fruit; Lower Midland (LM)-maize, sorghum, millet, sunflower, beans (tepary), cowpeas, black and green grams, chicken peas, pumpkin; Upper Midland zones (UM)-coffee, avocado, onions, cabbages, macadamia nuts, castor, bananas, pawpaws, citrus, sunflower and maize [32].
The three main forest types in the Taita Hills are indigenous montane forests, plantation forests of Eucalyptus spp., Pinus spp. and Cypressus lusitanica, and woodlands consisting of both native and exotic species [32]. The total area of indigenous forest remaining is approximately 8 km 2 , plantation forests cover 26 km 2 , woodlands 64 km 2 , and cropland 375 km 2 [7]. Of the cropped areas, 70% is continuous cropland (little tree cover), and 30% is agroforestry [7], in which typical species are exotic Grevillea robusta, Mangifera indica, Persea americana, and native species such as Prunus africana [6].

Mount Kilimanjaro
Mount Kilimanjaro is located in north-eastern Tanzania, approximately 300 km south from Nairobi ( Figure 1). Annual rainfall varies with altitude; about 1200 to 2000 mm yr −1 is received in the highland area; 1000 to 1200 mm yr −1 in the midlands and 400 to 900 mm yr −1 in the lowlands. The montane zone, which is dominated by agroforestry systems above 1800 m, receives the highest rainfall.
Due to the fertile volcanic soils and favourable climate the area is densely settled and supports agriculture. Similarly, as in the Taita Hills, the eastern and southern parts receive more rainfall. Mount Kilimanjaro has three distinct agro-ecological zones: the highland zone occurring between 1200 and 1800 m characterized predominantly coffee-banana belt; the midland area, occurring between 900 and 1200 m, is predominantly a maize-bean belt, and; the lowlands that extend 700 to 900 m [33]. A half-mile narrow forest strip occurs above the coffee-banana belt, which was established in 1941 as a buffer forest along the lower edge of the montane forest to provide local people with timber and non-timber forest products, preventing incursion into the National Park [33].
The study is conducted along two transects encompassing a wide range of environmental, land use and social conditions. On both Mount Kilimanjaro and Taita Hills, the study transects are located on wetter, eastern aspects ( Figure 1).

Sampling Plots
The primary data collection and analytical framework is presented in a flow diagram. A standard size plot of 1 ha [34] was used for sampling biometric parameters for trees with diameter at breast height (dbh) ≥ 10 cm. Stratified random sampling was used to partition the study area into agroforestry and cropped lands. However, the distribution of plots in agroforestry areas and cropped land were random along the elevation gradients of Mount Kilimanjaro and the Taita Hills. Within these stratifications, plots were distributed randomly using simple

Estimation of Plot Carbon
The Above-Ground Biomass (AGB) was estimated using the DBH and tree height recorded and the Wood Specific Gravity (WSG) (g/cm 3 ) derived from the Global Wood Density Database [35] [36] for the tree species; either at species level or genus, or family depending on the available information. With these parameters, an allometric model [37] was used for estimating AGB.
Where ρ is the Wood Specific Gravity, D 2 is the dbh, and H is the height of the tree. According to [37], the above model performed well across forest types and bioclimatic conditions. After deriving the AGB for each tree, data was aggregated to plot level from which 50% of AGB was assumed to be the above-ground carbon storage in a plot [37] [38] [39]. The standard unit for the amount of the above-ground carbon in this study is expressed in Megagram Carbon per hectare (Mg C ha −1 ).

Environmental Variables
To understand the relationships between environment, soil, vegetation and AGC and soil bulk density (BLD) ( Table 1).

Data Extraction and Statistical Modelling
Point values were extracted from the environmental variable layers using point analyses tool in ArcGIS 10. These were aggregated under major land use systems for Kilimanjaro and the Taita Hills for statistical analysis using the R programme [43]. Data relationships were described using univariate statistics and portrayed as series of boxplots to describe the amount of variation in agroforestry and cropped land in Kilimanjaro and the Taita Hills. Fischer's F-test (var test) was used to test the significance of the variation of data, and Student's t-test (t test) was used to test the significance difference in the data means between agroforestry and cropped land within sites; agroforestry, and cropped land between sites.

Aboveground Carbon on Sites
The mean Aboveground Carbon (AGC) along the Kilimanjaro transect is 39.06 ± 6.48 Mg C ha −1 (mean ± SE) and along the Taita Hills transect the mean is 28.82 ± 5.82 Mg C ha −1 ; both areas having comparable variation and means (   Table 2). Thus, the mean of AGC in agroforestry from the Kilimanjaro is 30%   Figure   3, Table 2). The variation of AGC in the cropped lands in the two sites are significantly different (F = 10.92, p = 0.020) while their means is not significantly different ( Figure 2, Table 2). The mean and median of AGC data in agroforestry areas in the Taita Hills are very close 31.98 ± 7.40 Mg C ha −1 (median 32.56). The boxplot indicate plots with small and large amount of AGC in agroforestry are heterogeneous but plots with moderate amount of AGC are homogeneous along the elevation gradient ( Figure 2, Table 2).
Most of the AGC data in cropped land are distributed towards the 1st quartile (13.24); thus, the mean (22.43 ± 5.49) and median (15.62) of AGC are not close.
However, variation of AGC data for cropped lands in the Taita Hills is both below the 1st and 3rd quartiles ( Figure 2, Table 2). The AGC in agroforestry on Kilimanjaro area is more than agroforestry in the Taita Hills; Tukey's pairwise comparisons shows the two sites are significantly different in the amount of AGC (Table 2). No significant difference is observed in the amount of AGC in cropped lands on Kilimanjaro and the Taita Hills.

AGC vs Environmental Variables in Types of Cropland
The response of AGC with EVI in agroforestry in Kilimanjaro is very significant and the relationship is fitted by the 2nd order of polynomial (R 2 = 0.92, p = 0.020) ( Figure 3, Table 3)

. The relationship of AGC and CEC in agroforestry in
Kilimanjaro is significant and the relationship is fitted by the 2nd order of polynomial (R 2 = 0.91, p = 0.027) ( Figure 3, Table 3). The response of AGC with BLD in agroforestry in Kilimanjaro shows significant relationship (R 2 = 0.87, p = 0.045), fitted by the 2nd order of polynomial ( Figure 3, Table 3). The relationship between AGC in agroforestry from the Taita Hills with the above variables are strong but are not significantly related ( Figure 3, Table 3). The response of AGC to EVI, CEC and BLD in cropped lands on Kilimanjaro and the Taita Hills are weak and does not show significant relationships ( Figure 3, Table 3).
The relationship of AGC and pH in agroforestry in the Taita Hills is very strong and significant (R 2 = 0.98, p = 0.031), fitted by the 3rd order of polynomial ( Figure 3, Table 3). A significant relationship occurs between AGC and SOC in cropped land in the Taita Hills (R 2 = 0.70, p = 0.00), fitted by 1st order of polynomial. On the other side, the relationship of SOC and AGC in cropped land on Kilimanjaro is weak and not significant ( Figure 3, Table 3).

AGC vs Woody Plant Species along Elevation Gradients
The distribution of the AGC among the woody plant species on the slope of Mount Kilimanjaro is predominated by Albizia gummifera with carbon storage of 8.6 MgC/ha; this is followed by Persea americana (3.5 MgC/ha) and Ficus sycomorus (3.3 MgC/ha) (Figure 4(a)). In the Taita Hills, the predominant woody species is Grevillea robusta with AGC of 4.6 MgC/ha; followed by Mangifera Journal of Geographic Information System

Statistical and Visual Validation of AGC Prediction Model
The evaluation considers models that show significant relationship with the dis-  (Table 5). In the Taita Hills, the model TCarbMod3 performs best in explaining the response of AGC (AIC = 71.11) and apparently associate with the high proportion of AGC recorded on site (R = 0.79, p = 0.01) than other models (Table 5). This is followed by SOC model O. Dickens et al. (AIC = 71.58) with the predicted AGC correlates with 77% of AGC recorded on plots frim the Taita Hills (R = 0.77, p = 0.01) ( Table 5).
slope, TCarbMod1 and TCarbMod2 are also comparable visually and this could probably be because they have slope as a factor in the multivariate models ( Figure 7, Table 5). However, models TCarbMod_SOC, TCarbMod_EVI and TCarbMod3 ( Figure 7, Table 5) has predicted AGC values and observed plot AGC comparable which makes them better models for predicting AGC spatial on the inhabited slopes of the Taita Hills.

Discussion
Carbon storage in the aboveground living biomass of trees forms the largest pool and the most directly impacted by deforestation and degradation [44]. Tropical montane forests have in the last centuries and decades been converted to other land uses or degraded [45]. Cropland established on previously sparsely vegetated or highly disturbed lands can result in a net gain in both biomass and soil carbon [46]. Permanent cropland is considered to be a broad category that con-Journal of Geographic Information System sists of cropped land, agroforestry and rice paddies [1]. Cropped land consists of the annual crops and or mono-cropping systems; while, agroforestry is characterized by mixed annual and perennial crops with fruit trees and trees of socio-economic benefits in the background. One of the key issues addressed in this study concerns the above-ground carbon stored in different types of cropped lands and agroforestry systems along the densely inhabited slopes of Mount Kilimanjaro and the Taita Hills.
The inhabited montane area of Mount Kilimanjaro has 39.06 ± 6.48 Mg C ha −1 and the Taita Hills 28.82 ± 5.82 Mg C ha −1 occurring within the mean range of AGC in East Africa ranging between 0.6 Mg C ha −1 in permanent cropland [9] to 91.5 Mg C ha −1 in agroforestry system [20]. A recent study in the Taita Hills suggest cropland has an average of 9.1 Mg C ha −1 in areas above 1220 m a.s.l. and 2.3 Mg C ha −1 below 1220 m a.s.l. which is considerably lower than our result [7].  [21] and the amounts of carbon storage in sub-humid and semi-arid ecoregions [47]. However, AGC in the woodlots in the cropland areas range from 39.4 to 123 Mg C ha −1 [9] which is higher than AGC in ordinary agroforestry areas. Pellikka et al. [7] found increase in carbon stocks in croplands over 1200 m. land [46]. The difference in AGC in cropped land and agroforestry areas in the two sites also depends on the crop type, management practices, and soil and climate variables in the croplands [48]. Cropped lands consist of treeless monocrop stand with few trees on hedges; while, agroforestry systems are characterized by vegetation structure that falls below the thresholds used for the Intergovernmental Panel on Climate Change Forest Land Category [2].
In this study, AGC is not only compared between sites but also analyzed against elevation gradients within each and between the sites. Most studies indicate the above-ground biomass and carbon stock significantly decreases with the increase in elevation [38]; and varies strongly between and within continents [47]. Elevational gradient causes great variation in vegetation structure and car- This research has wider application in understanding the drivers behind carbon storage, within agroforestry systems, biodiversity conservation and water conservation on the inhabited slopes of Mount Kilimanjaro and the Taita Hills.

O. Dickens et al. Journal of Geographic Information System
Water balance is a variable which is affected by carbon storage (AGC and SOC) and should be a consideration in the water conservation policy and management of montane forest systems as a key hydrological resource [50]. Understanding factors that relate with the AGC at a local scale provide an efficient means of monitoring AGC in a large area of study for future conservation and for enhancing the management of agroforestry systems. Information on AGC on the inhabited slopes provide baseline for future assessment of carbon emissions through removal of the woody plants and loss of soil organic carbon through land degradation. Management of carbon sequestration within densely inhabited landscapes can be adopted through agroforestry development such as the careful selection of tree species that are preferred at a local site by local communities and have good storage potential. The study shows that most of the AGC is contributed by agroforestry exotic tree species but indigenous species constitute low percentage which potentially affects biodiversity conservation. Consideration of indigenous tree species in agroforestry policy will bring about a balance between biodiversity conservation and socio-economic of the local communities on the inhabited slopes of Mount Kilimanjaro and the Taita Hills.

Conclusion
The