Economic assessment of selected regulatory ecosystem services (RES) in the Elgeyo and Nyambene Watersheds Ecosystems in Kenya

There is evidence of increased valuation of ecosystem services (ES) globally, however most of these studies tend to focus on marketed subsets of ES at national and international levels. Ecosystems differ in spatial scale, biophysical and ecological structure and functionality. This requires conducting studies at the local level to understand how, for example, the watershed ecosystem contributes to humanity both locally and nationally. This study focuses on selected regulatory ecosystem services (RES) in two catchment area ecosystems (Elgeyo and Nyambene) in Kenya. Both eld-based sampling and Landsat imagery with secondary information were used to generate biophysical and ecological data. Market price-based, cost-based and unit transfer methods were used for the valuation. Aggregated economic values for the selected RES were estimated at KES 37.4 billion (US$349.6 million) and KES 14 billion (US$131.3 million) for Elgeyo and Nyambene respectively. This equates to KES 1.5 million (US$13,848.48) and KES 2.6 million (US$24,187.44) per hectare per year. At the national level, the value of regulatory ecosystem services would range from US$16.6 billion to US$29.03 billion. This equates to between 15% and 26% of Kenya’s GDP in 2021, underscoring the importance of watersheds to the national economy.


Background
Ecosystem services (ES) are bene ts acquired by societies from natural ecosystems (Costanza et al., 1997;Daily, 1997;MA, 2005) that, in addition to ecological functions, are critical to sociocultural, economic, and human well-being (R. de Groot et al., 2012; Deal et al., 2012).Global forest ecosystems support millions of populations, particularly neighbouring communities, by providing both tangible and intangible bene ts thereby supporting their livelihoods and local economies (Boyd & Banzhaf, 2007;Deal et al., 2012).Tangible bene ts include fresh water, food, medicine, and fuelwood, while intangible bene ts include a pleasant landscape, improvement in global climate, wildlife habitat, and regulation of atmospheric gas chemistry (Daily, 1997;Deal et al., 2012;MA, 2005;Raymond et al., 2009;Vo et al., 2012).Despite the immense contribution to humankind, the contribution of ecosystem services remains invisible in socioeconomic, policy and development discourses (MA, 2005;Smith et al., 2013), and many decisions about such ecosystems are made without considering their actual monetary value (Mwaura et al., 2016).
The forested ecosystem covers 4.4 million hectares (7.7%) of the total land area of Kenya (FAO, 2015).With approximately 2.4 million hectares being state forest (The Republic of Kenya, 2018) with 1.2 million hectares under closed canopy forest (watersheds) (MoE&F, 2018).However, it has been reported to contribute about 3.6% to gross domestic product (GDP), with only 1.3% of its contribution is recorded and reported in national accounts (FAO, 2015).A small subset of ecosystem services is captured in national accounts, notably timber products, while forest-regulating ecosystem services (RES) and non-use services are largely ignored.This level of accounting does not capture the majority of ecosystem services and is therefore not re ected in decision-making processes (Emerton, 2014;Nahuelhual et al., 2007;TEEB, 2010).These measures arise for a number of reasons, including the poor ability to explicitly assess and quantify ecosystem bene t on-site (Costanza et al., 2017), the complexities associated with multi-scale and multi-dimensionality (de Groot et al., 2010), assessment approaches, data scarcity, and distortion of ES market among other challenges.The lack of data on the majority of ES values has made it di cult for conservationists and environmentalists to rationally argue their point, particularly in relation to promoting sustainable conservation, improved resource allocation, counteracting harmful strategies, and project implementation in watersheds.The invisibility of monetary values has been linked to setbacks in sustainable conservation (S.R. de Groot et al., 2002), overexploitation, degradation, and eventual decline in inventory and ow of bene ts (MA, 2005;Shaw et al., 2011) and impairment of social well-being (Barbier, 2015;Mutoko et al., 2015;van Jaarsveld et al., 2005).This is due to the fact that goods and services with no monetary value are unlikely to be considered in the conservation decision-making process.Although efforts are being made worldwide to include ES in spatial planning, governance and development discourse (Alamgir et al., 2016) to advance the appreciation of ecosystem services, little or nothing has been done at the local level.The valuation aims to determine the value of unmarketed ecosystem services individually or collectively.The overaching goal is to raise awareness to fundamentally change the way of thinking about 'a public good', 'a free' and 'a zero-nature' value (Mwaura et al., 2016).The mere listing of the ES without the necessary assignment of currency units forms the basis for the ES assessment (Costanza et al., 2017).However, an explicit assessment of ecological services would improve informed decisions, particularly in circumstances where trade-offs exist (
The Nyambene ecosystem is part of the Tana and Ewaso-Nyiro watersheds and covers 30,313 ha consisting of the state forest (5,427 ha) and the farmland within the ve-kilometer buffer zone (24,886 ha).The state forest is predominantly indigenous and divided into four management blocks including Nyambene, Kilimandingiri, Keiga, and Thuuri (KWTA, 2020c).The Nyambene extends from 0º 17' N to 0º 8' N, and from 37º 48' E to 37 º 52' E within Meru County and is traversed by the subdistricts of Igembe South, Igembe Central, Tigania East, Tigania West, and Tigania Central (Fig. 2).The ve subdistricts have a population of 691,298 (173,743 households) (KNBS, 2019).The precipitation regime is binomial with long rains between March and May and short rains in October and November and a mean of 1700 mm.
The altitude of the area ranges from 1000 m to 2,528 m above sea level while temperatures range from 13.7°C to 28.7°C.The ecosystem is endowed with oral diversity, over 200 springs, and a signi cant number of streams and rivers that serve as water sources for populations within the watershed and further downstream (KWTA, 2020c).

Research And Sampling Design
The study adopted a cross-sectional design with the actual assessment based on ecosystem service type, data, and bene t cohorts.Based on the classi cation of the Millennium Ecosystem Assessment (MEA) and the TEV framework, the ES data collection was regrouped into three perspectives, socio-cultural, ecological, and economic values.The socio-cultural values comes from households, focus group discussions, participatory mapping tools, expert surveys and the Q methodology.The ecological values using GIS and remote sensing were validated with eld studies and substantiated with secondary data, while the economic data leading to monetary allocations used traditional valuation techniques such as market prices, cost-based, stated and revealed preference techniques, and bene t transfer (Baral et al., 2017) to estimate the ES currency unit.However, the study focused on regulatory and support services and used a hybrid approach with both biophysical and socioeconomic attributes (Mengist et al., 2020), as shown.

Assessment of Ecological Values
The valuation of RES involved biophysical quanti cation and attribution of the monetary unit using nonmarket valuation techniques to assign monetary values.The evaluation technique used was based on study size, data needs, availability, available resources, topics, and available expertise (Baral et al., 2017;Burkhard et al., 2010Burkhard et al., , 2012;;Häyhä et al., 2015;Paudyal et al., 2015).The assessment began with land use land cover, RES pro ling and quantifIcation, attribution of shadow prices to products, and estimation of the grand total.

Land Use Classi cation
The study used Geographic Information System (GIS) and Remote Sensing (RS) techniques with a spatial resolution of 30 m to generate land cover data for the two ecosystems.The assessment began with image generation, image processing, classi cation with random forest classi er and creation of coresponding classi ed maps (LC1990, LC2000, LC2010, LC2020).Four Landsat path/array satellite images from three types of sensors were downloaded from the United States Geological Survey (USGS) website https://earthexplorer.usgs.gov/.The images were taken during the dry season of the year i.e. between January and March to ensure cloud-free and improved image display.The data was processed using ArcGIS 10.7 and R Studio 1.4.1106 and ENVI 5.3.The generated images were projected onto the Universal Transverse Mercator (UTM) coordinate system, datum Arc1960, Zone 36 North, and corrected for geometric errors from the sources using ground control points derived from a 1:50,000 scale topographical map.The other three previuos versions (L5 TM, L7 ETM+, L7 ETM+) of Landsat imagery (1985 TM, 2001 ETM+, and 2010 ETM+) were then each referenced by performing the frame-to-frame registration method using the latest version corrected Landsat 8 OLI /TIRS 2022 image.The IPCC Scheme II classi cation has been adopted, which considers ten (10)  The process began by delineating the training site with polygons, encoding the land cover, and enhancing the image features using true and false color composite.Validation of the prede ned land cover Landsat imagery training site was performed through eld observations from the 100 assessment points per ecosystem, Google Earth imagery, and historical land cover data generated through interviews with the adjacent community.Arandom forest classi er was applier with an accuracy of 0.8 based on the class confusion matrix to create a spectral signature and classi cation of all pixels in the generated image.
Finally, an image lter was applied to smooth the classi cation results by removing 'salt' and 'pepper' noise from the classi ed maps.The nal land cover maps were used to generate and analyze the LCLU class area size (ha) using the 'Tabulae' area algorithm in ArcGIS version 10.7 which intersects the imagery with the respective study area.

Water ow regulation and Water Puri cation
The study opted for the water storage method with replacement costs as indicated (1), as widely accepted (Langat et al., 2020;Langat, 2016;Xi, 2009) based on the avoided cost principle.Landcover size was determined using 2019 Landsat imagery, while precipitation amount was based on average annual precipitation data obtained on request from the Kenya Metrology Database (MoE&F, 2020).Runoff reduction coe cients were obtained from secondary databases of ecosystemS with similar ecological characteristics (Kateb et al., 2013;Okelo, 2009)  The function of the water puri cation ecosystem was based on the avoided water treatment costs according to formula (2).The amount of puri ed water was based on estimated annual precipitation retained by the two ecosystems.The unit cost of the puri cation function was based on the unit cost of constructing and maintaining a backup facility (municipal water treatment plant) (Jahanifar et al., 2017).This was based on the assumption that the destruction of the forest ecosystem would result in water quality degradation, which would require the construction of a municipal wastewater treatment plant to replace the ecosystem function.

2
Where V WQ represents the economic value to regulate the water quality of the ecosystem; Q WC is the amount of water stored and puri ed by the ecosystem, which can also be represented by total household consumption; ρ represents the unit cost of US$0.3/m 3 (Fuente et al., 2015) of the replacement water treatment mechanism

Soil Conservation And Erosion Control
In the study, the relative soil loss of land cover was assumed to be the unit cost of impact mitigation given by formula (3) on the avoided cost principle (Bishop, 1999;Nahuelhual et al., 2007).Land cover ×ρ size was determined from the 2019 land cover Landsat imagery, while the corresponding land cover soil erosion reduction coe cient was from the secondary database (Hurni, 1988;Kateb et al., 2013;Tessema et al., 2020).The unit cost of the ecosystem's soil erosion control function was based on the replacement cost of dredged water reservoirs, in this case a hydroelectric power generation dam estimated at US$3.34 per tonne of sediment (Adeogun et al., 2016).

3
Where V SC represents the economic value for forest soil protection; LC A is the respective land cover area (ha); SE RC is the soil erosion reduction coe cient based on land cover soil erosion coe cients (Hurni, 1988 Soil Nutrient Conservation (Dup: Abstract ?) In the study, it was assumed that the loss of in situ soil minerals (nitrogen (N), phosphorus (P), potassium (K)) is attributed to the relative soil loss across the different land covers and the unit replacement costs formula (4), as is often assumed in similar studies (Nahuelhual et al., 2007).Soil mineral content across different land covers was determined by eld sampling and laboratory analysis, while the unit value of the ecosystem soil nutrient protection function was equated to a surrogate (arti cial fertilizer) relative unit cost based on a replacement cost principle (Gizaw et al., 2021).
Where: EV SNC is the economic value of soil protection; S LC is soil conserved (kg/ha) of the respective land cover; SNC LC is the soil nutrient content (%) (N, P, K) in the forest soil; Q CF is the commercial fertilizer estimated at 150 kg/acre annually in Kenya; and CF is the ratio of commercial fertilizers (51%, NPK-17-17-17); P CF is the unit price of the commercial fertilizers (KES 60/kg)

Tree Carbon Quanti cation
The study used a generalized improved pantropical mixed species model (5) to estimate above-ground biomass (AGB).Tree biomass assessment targeted two main carbon pools (stem and root biomass) for each tree with a DBH ≥ 5 cm.Field-based sampling with a nested concentric plot design was used to measure tree dimensions (including tree height, diameter at breast height and crown diameter).The outer circle radius 15 m was used to record and measure trees with DBH ≥ 20 cm, while a 10 m radius was used to measure trees with DBH ≥ 10 < 20 cm, and a radius 5 m was used to record and measure V SC = ∑ LC A ×SE RC ×C Proxy parameters for trees with a DBH ≥ 5 cm while a 2 m radius was used to measure trees with a DBH < 5 cm and seedlings.

5
Where AGB is the above-ground weight of the tree (kg), ρ is the wood density D is the diameter at breast height in cm, H is the tree height while α and β are the model coe cients.
Total tree biomass was estimated by multiplying aboveground biomass by 1.25 (Chavan & Rasal, 2010).Aggregated carbon accounts for approximatly 47% of total biomass (IPCC, 2006(IPCC, , 2019)).The respective wood densities was obtained from the wood density database (Zanne et al., 2009).Wood speci c gravity was a particularly important predictor of AGB, especially when considering a wide range of vegetation types (Chave et al., 2014).The market prices were then used to estimate the economic value of the aggregated ecosystem carbon.

Soil Carbon Quanti cation
Soil carbon stocks were estimated from both soil organic carbon (SOC) and soil organic matter (SOM) levels.Organic matter (OM) content was determined using the loss on ignition method (LOI) while organic carbon (OC) was calculated using a ratio of 1:0.58 (SOM:SOC).Before the actual processing, a soil sample preparation was carried out.Samples were oven dried, crushed in mortar and pestle for homogenization, then sieved with a 2mm sieve to remove debris and stones, which were weighed separately.After sieving, the soil samples underwent a dry burning process required for carbon analysis to remove residual moisture.Two samples, each weighing 10 grams were placed in a pre-weighed crucible and then burned at 550°C for a minimum of 8 hours and then cooled before their weights were recorded.The difference in weights of the soil before and after heating represented the moisture and organic matter content, while the residue represented the ash.Soil organic carbon (SOC) was estimated by multiplying the weight difference by a factor of 0.58 as given in formula (6), while carbon per unit area was calculated by multiplying the SOC by the respective soil coe cients as given in formula (7).

7
Where S OC is soil organic carbon (%); IS is the initial weight of the soil sample; SR weight of soil residue after inceneration; T OC is total organic carbon (Mg of C per ha); ρ is the bulk density (g/cm); D is the soil tread depth (cm).
In mass calculations, soil samples were weighed for wet weight, air-dried at approximately 40°C for 48 hours, with an aliquot of each sample taken after weighing the air dried samples.The samples were further oven dried at 105°C for twenty-four hours and their weights were recorded.A total of three weights were recorded for each sample (i.e. total soil weight, the weight of the aliquot before oven drying at 105°C, and the weight after oven drying at 105°C) allowing calculation of bulk density.
The study used the market pricing function (Pearce, 2001) as indicated (8) to determine the value of forest carbon sequestration in contrast to the climate change damage function (Ferarro et al., 2011) with potential value overestimation.8 Whereby V FCR is the economic value for climate regulation, A LC is area (ha) of the respective land cover, Q C is the amount of carbon dioxide sequestered by the respective land cover per unit area, while C represents the average global carbon market price per unit of carbon.
Prices in global compliance markets currently range from less than US$1/tCO2e to US$30/tCO2e (AU$1-29/tCO2e).While considering the voluntary markets, average prices range from US$1/tCO2e to $5/ tCO2e or (AU$1-6/ CO2e)(World Bank Group, 2020).However, the study chose to use $5 per tonne of CO 2 as the prevailing price for carbon traded in Kenya in the Voluntary Carbon Standard (VCS) REDD + market.

Data Analysis
Descriptive statistics in SPSS were used to summarize data in measures of central tendency, spread, and variance.The data were examined to establish agreement with normal distribution assumptions, and using either ANOVA or Friedman's test depending on the examination outcome, to establish the relationship between study area parameters, for example land use as the independent variable and biomass and soil carbon as dependent variables.If necessary, a logarithmic transformation was carried out in order to comply with the normal distribution assumptions where necessary.

Land cover land use
The dominant land cover/use in the eight state forest blocks of the Elgeyo ecosystem in 2019 was dense forest at 41% (natural and exotic) followed by cropland at 36% and grassland at 22% grassland and shrub land.While the dominant land cover in the Nyambene state forest was dense forest (92%) and cropland (6%) (Table 1).

Watershed Protection
The study found that with a mean annual rainfall of 1200 mm (Elgeyo) and 1400mm (Nyambene), the two ecosystem store about 70 million and 39 million cubic meters of rainwater annually, respectively.
This translates to about 5400 m 3 and 7200 m 3 per hectare per year for Elgeyo and Nyambene.Using an average construction unit cost of US$3 per m 3 of arti cial water reservoir (dam) replacement, the total annual protection/storage value of the watershed would be estimated at KES15.6 billion (US$146.2 million) and KES8.6 billion (US$81 million) for the Elgeyo and Nyambene ecosystems, respectively.These corresponds to KES 620,200/ (US$5,796.3)and KES 1,600,000/ ($14,953.30)per hectare per year for Elgeyo and Nyambene, respectively (Table 2).The study's estimates were higher compared to a study in the Mau East ecosystem (Langat, 2016), whch reported a watershed protection value of KES 127,893.11(US$1,421.03)ha − 1 yr − 1 , and a study in Indonesia which water ow regulation and maintenance services were valued at US$1880 ha − 1 yr − 1 (range of $707-3110 ha − 1 yr − 1 )(Aulia et al., 2020).Similarly, the study estimates were higher than the study value in China (Xi, 2009) between US$540 and US$560 per hectare per year.The variance could be attributed to the difference in runoff coe cients, mean annual precipitation, forest cover, and unit cost of the replacement reservoir which vary by ecosystems and jurisdiction.The replacement unit cost only took into account the costs for the construction of the reservoir, but not the operating and administration costs of the reservoir.Likewise, the study only considered the water conservation value for state forests and not the forest value for adjacent community agricultural land.3).Study estimates for Elgeyo were within compared to a study in China (Xi, 2009) which ranged from US$999.55 to US$1,149.84 in water quality improvement per hectare per year.However, the area estimates for the Nyambene ecosystem were slightly higher compared to the reference study.Likewise, the results also contradicted the results of a study in Mau East with an estimated water quality regulation value of US$12 per hectare per year.The difference could be attributed to the different water data used for the Mau East case, where domestic water use data was used, while the study used the potential stormwater conserved by the ecosystem.The difference from the study in China could be attributed to percentage land cover and land use and inevitable price uctuations.found its way into the water reservoir, in contrast to the Mau, Cherangany and Elgon study, which assumed that only 50% made its way into the aquatic environment.

Soil Nutrient Conservation
The soil nutrient assessment across the different soil covers in the Elgeyo ecosystem recorded plant nutrients as shown (Table 5) with the mean being 0.91%, 12.5 mg/kg, and 7.9% for N, P, and SOC, respectively.9).This amount is much higher than that of forest tree carbon, demonstrating the signi cant potential of soils in this ecosystem to store signi cant amounts of carbon stocks.This demonstrate the importance of forest ecosystems in global carbon sequestration, and hence the need for increased conservation and advocacy of sustainable soil management of such ecosystems, thereby improving carbon stocks, resilience, productivity and livelihoods.for Elgeyo and Nyambene respectively.This would be estimated at aggregate value of KES 18.3 billion (US$ 171.3 million) and KES 3.5 billion (US$ 32.5 million) for the Elgeyo and Nyambene ecosystems respectively.The results show the enormous economic opportunity that the two ecosystems offer, especially for climate protection efforts through forest protection.Although the unit estimates are lower compared to most of the literature, the study provides the necessary data that can elicit establishment of programs such as payment of ecosystem service (PES) and REDD+.This can impact not only nature conservation but also the livelihoods of local communities and societies and thus a socio-economic situation conducive to development.
Furthermore, the analysis of the study results revealed different unit area values for the two ecosystems, a clear indication that different vegetation structures weight differently in terms of stand and ow of ES.
The largely natural Nyambene forest had higher area values compared to the largely exotic Elgeyo forest.
Based on the results, and since the assessment involves assessing trade-offs, converting natural forests to forests would signi cantly reduce the bene ts that society would have derived from the natural ecosystem.Notwithstanding the shortcomings, such studies should stimulate efforts to improve assessment techniques and approaches and continuously collect data to build a national ES database.In essence, the well-resourced database will be important to complement other conservation efforts and provide visibility to ecosystem services and detriments, thus supporting and facilitating socio-cultural and economic input into decision-making, resource accounting and policy discourse.
The results are also likely to spark a political debate on why a natural forest should be enhanced and why industrial forests should be planted in low land regions rather than upland areas that serve as watersheds.What type of seedlings should be particularly encouraged in the restoration of watershed in the country?Even if the the study results are not be absolute, they can form the basis for improved nature conservation.
Similarly, the study results, demonstrate the potential economic value of the two ecosystems, particularly for tradable products such as carbon.Taken together, such results would demonstrate the magnitude of the two ecosystems' bene ts to society and the potential market value for tradable products, and as such justify the need for increased investment, particularly in the conservation of the country's watersheds.The study estimates also provide the information that complements other local studies and would be crucial as society strives to include ES in the sociocultural and economic development discourse.Otherwise, a society is better off with inaccurate ES scores than without, since almost every decision-making process involves trade-offs and therefore some form of evaluation is required to make a choice.
Notwithstanding, ES assessment studies must strive to generate accurate and concise data to support conservation decision-making.Therefore, more needs to be done to provide the necessary and ecosystem/site-speci c information to build a national database that will be available in the future to policymakers, development partners, scientists and the sustainability-oriented community on the use of a country's natural ecosystems, particularly watersheds.

Declarations
Figures Braat & de Groot, 2012; de Groot et al., 2010), and highlight the costs of ecosystems and biodiversity loss (Di Franco et al., 2021).Forest ecosystems such as Elgeyo and Nyambene are essentially critical to the role they play in providing goods and services to society, particularly to neighbouring communities.The science of ecosystem services is still a new concept in Kenya and most studies have made extensive use of unit transfer (Seppelt et al., 2011).Using such techniques has not demonstrated the variability of ES supply and ux across different forest types, land cover, environmental gradients, and vegetation attributes (Alamgir et al., 2016).Since ES supply and ux vary by landscape, vegetation type and their respective properties, it becomes necessary to explicitly verify ES values at the local ecosystem level (Baral et al., 2014; Burkhard et al., 2012; de Groot et al., 2010; S. R. de Groot et al., 2002; Garcia-Nieto et al., 2013; Muller & Burkhard, 2012; van Oudenhoven et al., 2012).This therefore required the assessment of ecosystem services and aggregated values for the two watersheds (Elgeyo and Nyambene) in Kenya due to the lack of data on their ES values.The contribution of such a study will serve to raise awareness by making visible the monetary value of ES originating from such critical ecosystems in the country.Likewise the results will feed into the growing assessment database in Kenya with reported aggregate unit values ranging from US$1000 to about US$16,000 per ha per year (Kipkoech et al., 2011; D. Langat & Cheboiwo, 2010; D. K. Langat, 2016; D. K. Langat et al., 2020; Mwaura et al., 2016; Mwaura & Muhata, 2009).The monetary values clearly show the immense contribution of the watershed to the livelihood of the communities and the local economy.

1 V
and relative land cover coe cients (Blume et al., 2007; Goel, 2011; Karamage et al., 2018; Kauffman et al., 2007).The unit costs of the water regulation were determined by the unit costs of the replacement system (arti cial dam) (US$3/m 3 ) based on a replacement cost principle (Eytan & Spuhler, 2020; GOK & World Bank Group, 2005; Wu et al., 2010).WP represents the economic value for the watershed; A LC represents the area (ha) of land cover; P C represents the average annual rainfall that the ecosystem receives; RR coef .runoff reduction coe cient of the respective landcover (estimated by the precipitation runoff coe cient of the respective landcover/land use subtracted from the runoff coe cient of the bare area); C Sur represents the unit cost per cubic meter of replacement water reservoir.

Table 1
Land cover/land use size with respective loss and c-values reference

Table 2
inconsistency of household water use data necessitated the adoption of the water conservation approach and replacement unit cost to estimate the value of the ecosystem's water puri cation function.In this regard, and based on their respective annual rainfall, the two ecosystems would potentially store about 70 million m 3 and 38 million m 3 of water for Elgeyo and Nyambene respectively.Using the The

Table 3
Ecosystem Water Puri cation Function Valuation

Table 4
Economic Valuation of Forest Soil Conservation

Table 5
(Langat et al., 2016)lik et al., 2010);Okelo, 2009) cover in ElgeyoThe conservation value of soil nutrients used the respective mean soil loss per unit area(Gizaw et al., 2021;Kateb et al., 2013;Okelo, 2009)and the relative mineral unit (NPK) contribution to the relative mineral composition of the soils, multiplied by the respective unit costs of substitutes (commercial fertilizers).Based on the Elgeyo soil mineral estimates and the relative soil loss per unit area, the annual conserved amount of nutrients that would otherwise be lost without the two forest ecosystems is estimated at 77,041.70 tons and 21,038.54tonsperyear.Using the average amounts of commercial fertilizers applied to nutrient-poor soils in Kenya (380 kg/ha) and unit prices (KES 60/kg), the value for the nutrient maintenance function of forest soils can be estimated at KES 149.2 million (US$1.4 million) and KES 40.8 million (US$400,000) annually for the Elgeyo and Nyambene ecosystems, respectively.Estimates range from KES 5,916.57(US$55.3)toKES7,510.13(US$70.2) per hectare per year (Table6).Study results were consistent with study values for the Mau, Cherangany, and Elgon watershed of $67.35, $53.67, and $89.41 ha − 1 yr − 1 , respectively(Langat et al., 2020).However, the estimates were higher than the results of the Chile study with a soil fertility conservation value of US$26.3 ha − 1 yr − 1(Nahuelhual et al., 2007)and lower than that of the Xishuangbanna corridors in China with a mean value of US$1,103.61ha− 1 yr − 1(Xi, 2009).The results agreed with the study from Anji County, Huzhou, Zhejiang, China on forest soil conservation based on the eco-service unit method with a mean value of RMB436 (US$69.8)ha− 1 yr − 1(Zhang et al., 2015).estimates of mean tropical forest carbon of 183 Mg/ha(Sullivan et al., 2017).The estimates are also lower than the biomass of most tropical African forests and Borneo, which is reported to be between 395.7 and 445 Mg/ha(Lewis et al., 2013;Slik et al., 2010).Notwithstanding, the study estimates for Nyambene were signi cantly higher than the Elgeyo carbon estimate and consistent with the Taita Hills study, which found a mean of 92.59 and 211.5 MgC/ha(Omoro et al., 2013).The discrepancy could possibly be attributed to forest degradation, deforestation, and conversion to other land uses as reported in the case of Elgeyo and a well-preserved ecosystem in case of Nyambene (KWTA, 2020b).US$2,385.87)perhectareperyear(Table8).Estimates for Elgeyo were lower and consistent with the Mengla-Shangyon and Nabanhe-Mangao corridor study (China) in the case of Nyambene with a unit value of US$2,195 per hectare per year(Xi, 2009).A similar scenario compared to the East Mau study that assessed carbon sequestration and was valued at $2,782.47 per hectare per year(Langat et al., 2016).The difference is mainly attributed to the degraded forest in the case of Elgeyo and the fairly well preserved forest in the case of Nyambene.Furthermore, the discrepancy could be attributed to the transfer of carbon units and the unit price used, with US$10 used in the two reference studies, while US$5 per CO 2 was used in this study.
2for Elgeyo and Nyambene, respectively.At a unit price of US$5, the total monetary value of the CO 2 sequestered by the two ecosystems in the soils can be estimated at KES 16.1 billion (US$150.85 million) and 2.6 billion (US$24.2million) respectively, for Elgeyo and Nyambene ecosystems.This equates to KES 639,866.42 (US$5,980.06)and KES 477,055.22 (US$4,458.46)per hectare per year (Table