SpringerLink
Log in

Spatially distributed modelling and map** of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve, Kenya

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

Detailed knowledge about the estimates and spatial patterns of soil organic carbon (SOC) and total nitrogen (TN) stocks is fundamental for sustainable land management and climate change mitigation. This study aimed at: (1) map** the spatial patterns, and (2) quantifying SOC and TN stocks to 30 cm depth in the Eastern Mau Forest Reserve using field, remote sensing, geographical information systems (GIS), and statistical modelling approaches. This is a critical ecosystem offering essential services, but its sustainability is threatened by deforestation and degradation. Results revealed that elevation, silt content, TN concentration, and Landsat 8 Operational Land Imager band 11 explained 72% of the variability in SOC stocks, while the same factors (except silt content) explained 71% of the variability in TN stocks. The results further showed that soil properties, particularly TN and SOC concentrations, were more important than that other environmental factors in controlling the observed patterns of SOC and TN stocks, respectively. Forests stored the highest amounts of SOC and TN (3.78 Tg C and 0.38 Tg N) followed by croplands (2.46 Tg C and 0.25 Tg N) and grasslands (0.57 Tg C and 0.06 Tg N). Overall, the Eastern Mau Forest Reserve stored approximately 6.81 Tg C and 0.69 Tg N. The highest estimates of SOC and TN stocks (hotspots) occurred on the western and northwestern parts where forests dominated, while the lowest estimates (coldspots) occurred on the eastern side where croplands had been established. Therefore, the hotspots need policies that promote conservation, while the coldspots need those that support accumulation of SOC and TN stocks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amare T, Hergarten C, Hurni H et al., 2013. Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy. ISRN Soil Science, 720589 (11 pp), http://dx.doi.org/10.1155/2013/720589.

    Google Scholar 

  • Aynekulu E, Vågen T-G, Shepherd K et al., 2011. A protocol for measurement and monitoring soil carbon stocks in agricultural landscapes. Version 1.1. World Agroforestry Centre, Nairobi.

    Google Scholar 

  • Batjes N H, 2004. Soil carbon stocks and projected changes according to land use and management: A case study for Kenya. Soil Use and Management, 20: 350–356.

    Article  Google Scholar 

  • Bewketa W, Stroosnijder L, 2003. Effects of agro-ecological land use succession on soil properties in Chemoga watershed, Blue Nile basin, Ethiopia. Geoderma, 111: 85–98.

    Article  Google Scholar 

  • Blake G R, 1965. Bulk density. In: Black C A (ed.). Methods of Soil Analysis, Part 1. Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling, American Society of Agronomy, Inc., Madison, Wisconsin, USA.

    Google Scholar 

  • Bremner J M, Mulvaney C S, 1982. Nitrogen-total. In: Page A L (ed.). Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. 2nd ed. American Society of Agronomy, Inc., Madison, Wisconsin, USA.

    Google Scholar 

  • Cambule A H, Rossiter D G, Stoorvogel J J et al., 2014. Soil organic carbon stocks in the Limpopo National Park, Mozambique: Amount, spatial distribution and uncertainty. Geoderma, 213: 46–56.

    Article  Google Scholar 

  • Chaplot V, Bouahom B, Valentin C, 2010. Soil organic carbon stocks in Laos: Spatial variations and controlling factors. Global Change Biology, 16: 1380–1393.

    Article  Google Scholar 

  • Day P R, 1965. Particle fractionation and particle size analysis. In: Black C A (ed.). Methods of Soil Analysis, Part 1. Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling. American Society of Agronomy, Inc., Madison, Wisconsin, USA.

    Google Scholar 

  • Demessie A, Singh B R, Lal R, 2013. Soil carbon and nitrogen stocks under chronosequence of farm and traditional agro-forestry uses in Gambo district, southern Ethiopia. Nutr. Cycl. Agroecosys., 95: 365–375.

    Article  Google Scholar 

  • Doetterl S, Stevens A, van Oost K et al., 2013. Spatially explicit regional scale prediction of soil organic carbon stocks in cropland using environmental variables and mixed model approaches. Geoderma, 204/205: 31–42.

    Article  Google Scholar 

  • Dorji T, Odeh I O A, Field D J et al., 2014. Digital soil map** of soil organic carbon stocks under different land use and land cover types in montane ecosystems, Eastern Himalayas. Forest Ecology and Management, 318: 91–102.

    Article  Google Scholar 

  • Eclesia R P, Jobbagy E G, Jackson R B et al., 2012. Shifts in soil organic carbon for plantation and pasture establishment in native forests and grasslands of South America. Global Change Biology, 18: 3237–3251.

    Article  Google Scholar 

  • Alkheir A et al., 2014. Spatial variation of soil carbon and nitrogen pools by using ordinary kriging method in an area of north Nile delta, Egypt. Catena, 113: 70–78.

    Article  Google Scholar 

  • Fotheringham A S, Brunsdon C, Charlton M E, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. England: John Wiley & Sons Inc.

    Google Scholar 

  • Girmay G, Singh B R, 2012. Changes in soil organic carbon stocks and soil quality: Land use system effects in northern Ethiopia. Acta Agriculturae Scandinavica, Section B -, Soil & Plant Science, 62(6): 519–530.

    Google Scholar 

  • Government of Kenya, 2009. Report of the prime minister’s task force on the conservation of the Mau forest complex. [Online]. Available: http://www.kws.org/export/sites/kws/info/maurestoration/maupublications/Mau_Forest_Complex_Report.pdf [Accessed 2014, January 19].

    Google Scholar 

  • Grimm R, Behrens T, Märker M et al., 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island: Digital soil map** using Random Forests analysis. Geoderma, 146: 102–113.

    Article  Google Scholar 

  • Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regression-kriging: From equations to case studies. Computers & Geosciences, 33: 1301–1315.

    Article  Google Scholar 

  • Hengl T, Heuvelink G B M, Stein A, 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120: 75–93.

    Article  Google Scholar 

  • Hiemstra P, 2013. Classes and methods for spatial data in R. [Online]. Available: http://cran.r-project.org/web/packages/automap/automap.pdf. [Accessed 2013, December 15].

    Google Scholar 

  • IPCC, 2006. IPCC Guidelines for national greenhouse gas inventories, prepared by the national greenhouse gas inventories programme, Eggleston H S, Buendia L, Miwa K et al. (eds.). Published: IGES, Japan.

  • Jaber S M, Al-Qinna M I, 2011. Soil organic carbon modelling and map** in a semi-arid environment using thematic mapper data. Photogrammetric Engineering & Remote Sensing, 77(7): 709–719.

    Article  Google Scholar 

  • Jaetzold R, Schmidt H, Hornetz B et al., 2010. Farm management handbook of Kenya, Vol. II. Natural conditions and farm management information. 2nd ed., Part B Central Kenya, Subpart B1a Southern Rift Valley Province. Ministry of Agriculture, Kenya and German Agency for Technical Cooperation (GTZ), Nairobi.

    Google Scholar 

  • Karunaratne S B, Bishop T F A, Baldock J A et al., 2014. Catchment scale map** of measureable soil organic carbon fractions. Geoderma, 219/220: 14–23.

    Article  Google Scholar 

  • Kheir R B, Greve M H, BØcher P K et al., 2010. Predictive map** of soil organic carbon in wet cultivated lands using classification tree-based models: The case study of Denmark. Journal of Environmental Management, 91: 1150–1160.

    Article  Google Scholar 

  • Kumar S, Lal R, 2011. Map** the organic carbon stocks of surface soils using local spatial interpolator. Journal of Environmental Monitoring, 13: 3128–3135.

    Article  Google Scholar 

  • Kumar S, Lal R, Liu D, 2012. A geographically weighted regression kriging approach for map** soil organic carbon stock. Geoderma, 189/190: 627–634.

    Article  Google Scholar 

  • Kumar S, Lal R, Liu D, 2013. Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA. Journal of Geographical Sciences, 23(2): 280–296.

    Article  Google Scholar 

  • Lacoste M, Minasny B, McBratney A et al., 2014. High resolution 3D map** of soil organic carbon in a heterogeneous agricultural landscape. Geoderma, 213: 296–311.

    Article  Google Scholar 

  • Lal R, 2004. Soil carbon sequestration to mitigate climate change. Geoderma, 123: 1–22.

    Article  Google Scholar 

  • Lamsal S, Grunwald S, Bruland G L et al., 2006. Regional hybrid geospatial modeling of soil nitrate–nitrogen in the Santa Fe River watershed. Geoderma, 135: 233–247.

    Article  Google Scholar 

  • Lemenih M, Karltun E, Olsson M, 2005. Assessing soil chemical and physical property responses to deforestation and subsequent cultivation in smallholders farming system in Ethiopia. Agriculture, Ecosystems and Environment, 105: 373–386.

    Article  Google Scholar 

  • Lesch S M, Corwin D L, 2008. Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests. Geoderma, 148: 130–140.

    Article  Google Scholar 

  • Li D, Shao M, 2014. Soil organic carbon and influencing factors in different landscapes in an arid region of north-western China. Catena, 116: 95–104.

    Article  Google Scholar 

  • Li M, Zhang X, Pang G et al., 2013b. The estimation of soil organic carbon distribution and storage in a small catchment area of the Loess Plateau. Catena, 101: 11–16.

    Article  Google Scholar 

  • Li Q, Yue T, Wang C et al., 2013a. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach. Catena, 104: 210–218.

    Article  Google Scholar 

  • Li Y, 2010. Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information? Geoderma, 159: 63–75.

    Article  Google Scholar 

  • Liu Z, Shao M, Wang Y, 2011. Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region, China. Agriculture, Ecosystems and Environment, 142: 184–194.

    Article  Google Scholar 

  • Malone B P, McBratney A B, Minasny B et al., 2009. Map** continuous depth functions of soil carbon storage and available water capacity. Geoderma, 154: 138–152.

    Article  Google Scholar 

  • Marchetti A, Piccini C, Francaviglia R et al., 2012. Spatial distribution of soil organic matter using geostatistics: A key indicator to assess soil degradation status in central Italy. Pedosphere, 22(2): 230–242.

    Article  Google Scholar 

  • Martin M P, Orton T G et al., 2014. Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma, http://dx.doi.org/10.1016/j.geoderma.2014.01.005.

    Google Scholar 

  • Martin M P, Wattenbach M, Smith P et al., 2011. Spatial distribution of soil organic carbon stocks in France. Biogeosciences, 8: 1053–1065.

    Article  Google Scholar 

  • McBratney A B, Santos M L M, Minasny B, 2003. On digital soil map**. Geoderma, 117: 3–52.

    Article  Google Scholar 

  • McCall G J H, 1967. Geology of the Nakuru-Thomson’s falls-Lake Hannington area: Degree sheet No. 35, S.W. Quarter and 43 N.W. Quarter, Report No. 78. Government Printer, Nairobi.

    Google Scholar 

  • McKenzie N J, Ryan P J, 1999. Spatial prediction of soil properties using environmental correlation. Geoderma, 89: 67–94.

    Article  Google Scholar 

  • Meersmans J, de Ridder F, Canters F et al., 2008. A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma, 143: 1–13.

    Article  Google Scholar 

  • Mehrjardi R T, Minasny B, Sarmadian F et al., 2014. Digital map** of soil salinity in Ardakan region, central Iran. Geoderma, 213: 115–128.

    Article  Google Scholar 

  • Mishra U, Lal R, Liu D et al., 2010. Predicting the spatial variation of the soil organic carbon pool at a regional scale. Soil Science Society of America Journal, 74: 906–914.

    Article  Google Scholar 

  • Mishra U, Riley W J, 2012. Alaskan soil carbon stocks: Spatial variability and dependence on environmental factors. Biogeosciences, 9: 3637–3645.

    Article  Google Scholar 

  • Montgomery D C, Peck E A, Vining G G, 2006. Introduction to Linear Regression Analysis. John Wiley & Sons, Inc., New Jersey.

    Google Scholar 

  • Mora-Vallejo A, Claessens L, Stoorvogel J et al., 2008. Small-scale digital soil map** in southeastern Kenya. Catena, 76: 44–53.

    Article  Google Scholar 

  • Murty D, Kirschbaum M F, McMurtrie R E et al., 2002. Does conversion of forest to agricultural land change soil carbon and nitrogen? A review of the literature. Global Change Biology, 8: 105–123.

    Article  Google Scholar 

  • Nelson D W, Sommers L E, 1982. Total carbon, organic carbon and organic matter. In: Page A L (ed.) Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties. 2nd ed. American society of agronomy, Inc., Madison, Wisconsin, USA.

    Google Scholar 

  • Obade V P, Lal R, 2013. Assessing land cover and soil quality by remote sensing and geographical information systems (GIS). Catena, 104: 77–92.

    Article  Google Scholar 

  • Okalebo J R, Gathna K W, Woomer P L, 2002. Laboratory methods for soil and plant analysis: A working manual. 2nd ed. Tropical Soil Biology and Fertility Programme, Nairobi.

    Google Scholar 

  • Overmars K P, Verburg P H, 2005. Analysis of land use drivers at the watershed and household level: Linking two paradigms at the Philippine forest fringe. International Journal of Geographical Information Science, 19(2): 125–152.

    Article  Google Scholar 

  • Pachomphon K, Dlamini P, Chaplot V, 2010. Estimating carbon stocks at regional level using soil information and easily accessible auxiliary variables. Geoderma, 155: 372–380.

    Article  Google Scholar 

  • Pebesma E, Bivand R S, Rowlingson B et al., 2013. Classes and methods for spatial data in R. Available: http://cran.r-project.org/web/packages/sp/sp.pdf. [Accessed 2013, December 15].

    Google Scholar 

  • R Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://wwwR-projectorg/.

    Google Scholar 

  • Razakamanarivo R H, Grinand C, Razafindrakoto M A et al., 2011. Map** organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach. Geoderma, 162: 335–346.

    Article  Google Scholar 

  • Scull P, Franklin J, Chadwick O A et al., 2003. Predictive soil map**: A review. Progress in Physical Geography, 27(2): 171–197.

    Article  Google Scholar 

  • Selige T, Böhner J, Schmidhalter U, 2006. High resolution topsoil map** using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma, 136: 235–244.

    Article  Google Scholar 

  • Smith P, 2004. Soils as carbon sinks: The global context. Soil Use and Management, 20: 212–218.

    Article  Google Scholar 

  • Smith P, 2008. Land use change and soil organic carbon dynamics. Nutr. Cycl. Agroecosyst., 81: 169–178.

    Article  Google Scholar 

  • Sumfleth K, Duttmann R, 2008. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological Indicators, 485–501.

    Google Scholar 

  • Szymanowski M, Kryza M, 2012. Local regression models for spatial interpolation of urban heat island: An example from Wroclaw, SW Poland. Theor. Appl. Climatol., 108: 53–71.

    Article  Google Scholar 

  • Tamooh F, van den Meersche K, Meysman F et al., 2012. Distribution and origin of suspended matter and organic carbon pools in the Tana River basin, Kenya. Biogeosciences, 9: 2905–2920.

    Article  Google Scholar 

  • Tesfahunegn G B, Tamene L, Vlek P L G, 2011. Catchment scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil & Tillage Research, 117: 124–139.

    Article  Google Scholar 

  • UNEP, 2009. Kenya: Atlas of Our Changing Environment. Division of Early Warning and Assessment (DEWA), United Nations Environment Programme (UNEP). [Online]. Available: http://www.unep.org/dewa/africa/kenyaatlas/. [Accessed 2013, August 28].

    Google Scholar 

  • Vågen T G, Winowiecki L A, 2013a. Map** of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential. Environmental Research Letters 8, 015011 (9pp). doi: 10.1088/1748-9326/8/1/015011.

    Google Scholar 

  • Vågen T G, Winowiecki L A, Abegaz A et al., 2013b. Landsat-based approaches for map** of land degradation prevalence and soil functional properties in Ethiopia. Remote Sensing of Environment, 134: 266–275.

    Article  Google Scholar 

  • Vasques G M, Grunwald S, Comerford N B et al., 2010a. Regional modelling of soil carbon at multiple depths within a subtropical watershed. Geoderma, 156: 326–336.

    Article  Google Scholar 

  • Vasques G M, Grunwald S, Sickman J O et al., 2010b. Up-scaling of dynamic soil organic carbon pools in a north-central Florida watershed. Soil Science Society of America Journal, 74. doi: 10.2136/sssaj2009.0242.

    Google Scholar 

  • Wang K, Zhang C, Li W, 2013. Predictive map** of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and co-kriging. Applied Geography, 42: 73–85.

    Article  Google Scholar 

  • Were K O, Dick Ø B, Singh B R, 2013. Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya. Applied Geography, 41: 75–86.

    Article  Google Scholar 

  • Were K O, Singh B R, Dick Ø B, 2015. Effects of land cover changes on soil organic carbon and nitrogen stocks in the Eastern Mau Forest Reserve, Kenya. In: Lal R, Singh B R, Mwaseba D L et al., (eds.). Sustainable Intensification to Advance Food Security and Enhance Climate Resilience in Africa. Springer International Publishing, Switzerland, 113–133.

    Google Scholar 

  • Wiesmeier M, Spörlein P, Geuβ U et al., 2012. Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth. Global Change Biology, 18: 2233–2245.

    Article  Google Scholar 

  • Winowiecki L, Vågen T G, Huising J, 2015. Effects of land cover on ecosystem services in Tanzania: A spatial assessment of soil organic carbon. Geoderma, http://dx.doi.org/10.1016/j.geoderma.2015.03.010.

    Google Scholar 

  • Wu C, Wu J, Luo Y et al., 2009. Spatial prediction of soil organic matter content using co-kriging with remotely sensed data. Soil Science Society of America Journal, 73: 1202–1208.

    Article  Google Scholar 

  • Yang R, Su Y Z, Wang M et al., 2014. Spatial pattern of soil organic carbon in desert grasslands of the diluvial-alluvial plains of northern Qilian Mountains. Journal of Arid Land, 6(2): 136–144.

    Article  Google Scholar 

  • Yang Y, Fang J, Tang Y et al., 2008. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Global Change Biology, 14: 1592–1599.

    Article  Google Scholar 

  • Zaehle S, Ciais P, Friend A D et al., 2011. Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions. Nature Geoscience, 4: 601–605.

    Article  Google Scholar 

  • Zhang C, Tang Y, Xu X et al., 2011. Towards spatial geochemical modelling: Use of geographically weighted regression for map** soil organic carbon contents in Ireland. Applied Geochemistry, 26: 1239–1248.

    Article  Google Scholar 

  • Zhang S, Huang Y, Shen C et al., 2012. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma, 171/172: 35–43.

    Article  Google Scholar 

  • Zhang Z, Yu C, Shi X et al., 2010. Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China. Soil Science and Plant Nutrition, 56: 307–318.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway

    Kennedy Were & Øystein Bjarne Dick

  2. Kenya Agricultural and Livestock Research Organisation, Kenya Soil Survey, P.O. Box 14733-00800, Nairobi, Kenya

    Kennedy Were

  3. Department of Environmental Sciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432, Ås, Norway

    Bal Ram Singh

Authors
  1. Kennedy Were
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bal Ram Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Øystein Bjarne Dick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kennedy Were.

Additional information

Author: Kennedy Were, PhD, specialized in application of GIS and remote sensing techniques in environmental research.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Were, K., Singh, B.R. & Dick, Ø.B. Spatially distributed modelling and map** of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve, Kenya. J. Geogr. Sci. 26, 102–124 (2016). https://doi.org/10.1007/s11442-016-1257-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-016-1257-4

Keywords

Search

Navigation