research paper on land degradation

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 13 November 2018

Land degradation and poverty

• Edward B. Barbier   ORCID: orcid.org/0000-0002-5354-3995 1 &
• Jacob P. Hochard   ORCID: orcid.org/0000-0002-9502-2602 2  

Nature Sustainability volume  1 ,  pages 623–631 ( 2018 ) Cite this article

3611 Accesses

174 Citations

19 Altmetric

Metrics details

• Developing world
• Environmental economics
• Environmental social sciences
• Sustainability

Land is one of the few productive assets owned by the rural poor, and almost all such households engage in some form of agriculture. Over 2000–2010 the rural poor on degrading agricultural land increased in low-income countries and in sub-Saharan Africa and South Asia. Although degradation threatens the livelihoods of the poor, this interaction is complex and conditioned by key economic, social and environmental factors. These factors also limit the poverty-reducing impacts of economic growth and economy-wide reforms. A comprehensive development strategy requires investments that improve the livelihoods of affected populations and regions, and facilitates outmigration in severely impacted areas.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Characterising the spatial distribution of opportunities and constraints for land sparing in Brazil

Socio-economic and environmental trade-offs in Amazonian protected areas and Indigenous territories revealed by assessing competing land uses

Influence of land tenure interventions on human well-being and environmental outcomes

Transforming Our World: The 2030 Agenda for Sustainable Development (UN, 2015).

World Commission on Environment and Development Our Common Future (Oxford Univ. Press, 1987).

Scherr., S. J. A downward spiral? Research evidence on the relationship between poverty and natural resource degradation. Food Policy 25 , 479–498 (2000). A path-breaking study on the multiple factors underlying the potential ‘downward spiral’ between poverty and environmental degradation .

Google Scholar  

Grainger, A. The Threatening Desert: Controlling Desertification (Earthscan Publications, 1990).

Reardon, T. & Vosti, S. A. Links between rural poverty and the environment in developing countries: asset categories and investment poverty. World Dev. 23 , 1495–1506 (1995). One of the first studies to emphasize the importance of understanding how household assets and conditioning factors interact to influence poverty–environmental degradation relationships .

Barbier, E. B. The economic determinants of land degradation in developing countries. Phil. Trans. R. Soc. B 352 , 891–899 (1997).

Duraiappah, A. K. Poverty and environmental degradation: a review and analysis of the nexus. World Dev. 26 , 2169–2179 (1998).

Crosson, P. in Encyclopaedia of Soil Science Vol. 2 (ed. Lal, R.) 399–402 (Taylor and Francis, London, 2006).

Gerber, N., Nkonya, E. & von Braun, J. in Marginality: Addressing The Nexus Of Poverty, Exclusion and Ecology (eds von Braun J. & Gatzweiler, F. W.) 181–202 (Springer, Dordrecht, 2014). A thorough and comprehensive review of the issues and challenges in assessing the links between land degradation and poverty globally .

Barrett, C. B. & Bevis, L. E. M. The self-reinforcing feedback between low soil fertility and chronic poverty. Nat. Geosci. 8 , 907–912 (2015).

CAS   Google Scholar  

Sklenicka, P. Classification of farmland ownership fragmentation as a cause of land degradation: a review on typology, consequences, and remedies. Land Use Policy 57 , 694–701 (2016).

Vogt, J. V. et al. Monitoring and assessment of land degradation and desertification: towards new conceptual and integrated approaches. Land Degrad. Dev. 22 , 150–165 (2011).

Adeel, Z. et al. Ecosystems and Human Well-Being: Desertification Synthesis. A Report of the Millennium Ecosystem Assessment (World Resources Institute, 2005).

Stavi, H. & Lal, R. Achieving zero net land degradation: challenges and opportunities. J. Arid Environ. 112 , 44–51 (2015).

Grainger, A. Is land degradation neutrality feasible in dry areas? J. Arid Environ. 112 , 14–24 (2015).

Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and Desertification Particularly in Africa: Final text of the Convention (UN, 1994).

Better Growth, Better Climate: The New Climate Economy Report Ch. 3 (Global Commission on the Economy and Climate, 2014).

Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304 , 1623–1627 (2004).

West, P. C. et al. Leverage points of improving global food security and the environment. Science 345 , 325–328 (2014).

Stocking, M. A. Tropical soils and food security – the next 50 years. Science 302 , 1356–1359 (2003).

Reynolds, J. F. et al. Building a science for dryland development. Science 316 , 847–851 (2007).

Lambin, E. F. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl Acad. Sci. USA 108 , 3465–3472 (2011).

Foley, J. A. et al. Global consequences of land use. Science 309 , 570–574 (2005).

Gisladottir, G. & Stocking, M. A. Land degradation control and its global environmental benefits. Land Degrad. Dev. 16 , 99–112 (2005).

Mirzabaev, A., Nkonya, E. & von Braun, J. Economics of sustainable land management. Curr. Opin. Env. Sustain. 15 , 9–19 (2015).

Barbier, E. B. & Hochard, J. P. Does land degradation increase poverty in developing countries? PLoS ONE 11 , e0152973 (2016).

Bai, Z. G., Dent, D. L., Olsson, L. & Schaepman, M. E. Proxy global assessment of land degradation. Soil Use Manage. 24 , 223–234 (2008).

de Jong, R., de Bruin, S., Schaepman, M. & Dent, D. Quantitative mapping of global land degradation using Earth observations. Int. J. Remote Sens. 32 , 6823–6853 (2011).

Nachtergaele, F., Petri, M., Biancalani, R., Van Lynden, G. & Van Velthuizen, H. Global Land Degradation Information System (GLADIS). Beta Version. An Information Database for Land Degradation Assessment at Global Level Land Degradation Assessment in Drylands Technical Report No. 17 (FAO, 2010).

Castañeda, A. et al. A new profile of the global poor. World Dev. 101 , 250–267 (2018).

Wood, S., Sebastian, K. & Scherr, S. Pilot Analysis of Global Ecosystems (PAGE), Agroecosystems dataset. Harvard Dataverse https://hdl.handle.net/1902.1/17463 (2005).

Gridded Population of the World Version 4 (GPWv4) (Center for International Earth Science Information Network, Socioeconomic Data and Applications Center, Columbia University, 2017); http://sedac.ciesin.columbia.edu/gpw

Global Rural-Urban Mapping Project Version 1 (GRUMPv1): Urban Extents Grid (Center for International Earth Science Information Network, Socioeconomic Data and Applications Center, Columbia University, 2011); http://sedac.ciesin.columbia.edu/data/set/grump-v1-urban-extents

Prince, S. D. & Goward, S. N. Global primary production: a remote sensing approach. J. Biogeogr. 22 , 815–835 (1995).

Global Subnational Infant Mortality Rates Version 1(2000) (Center for International Earth Science Information Network, Socioeconomic Data and Applications Center, Columbia University, 2015); http://sedac.ciesin.columbia.edu/data/set/povmap-global-subnational-infant-mortality-rates

de Sherbinin, A. Is poverty more acute near parks? An assessment of infant mortality rates around protected areas in developing countries. Oryx 42 , 26–35 (2008).

Sartorius, B. K. D. & Sartorius, K. Global infant mortality trends and attributable determinants – an ecological study using data from 192 countries for the period 1990–2011. Pop. Health Metrics 12 , 29 (2014).

Fritzell, J., Rehnberg, J., Bacchus Hertzman, J. & Blomgren, J. Absolute or relative? A comparative analysis between poverty and mortality. Int. J. Publ. Health 60 , 101–110 (2015).

New country classifications by income level: 2017–2018 World Bank https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2017-2018 (1 July 2017).

Higginbottom, T. P. & Symeonakis, E. Assessing land degradation and desertification using vegetation index data: current frameworks and future directions. Remote Sens. 6 , 9552–9575 (2014).

Barbier, E. B. Climate change impacts on rural poverty in low-elevation coastal zones. Estuarine Coastal Shelf Sci. 165 , A1–A13 (2015).

Barbier, E. B. & Hochard, J. P. The impacts of climate change on the poor in disadvantaged regions. Rev. Environ. Econ. Policy 12 , 26–47 (2018).

Pender, J. Agricultural Technology Choices for Poor Farmers in Less-Favoured Areas Of South And East Asia (International Fund for Agricultural Development, 2008).

Lufumpa, C. L. The poverty-environment nexus in Africa. Afr. Dev. Rev. 17 , 366–381 (2005).

Jayne, T. S., Chamberlin, J. & Headey, D. D. Land pressures, the evolution of farming systems, and development strategies in Africa: a synthesis. Food Policy 48 , 1–17 (2014).

Binswanger-Mkhize, H. & Savastano, S. Agricultural intensification: the status of six African countries. Food Policy 67 , 26–40 (2017).

Hazell, P. & Wood, S. Drivers of change in global agriculture. Phil. Trans. R. Soc. B 363 , 495–515 (2008). A comprehensive review of the current challenges and options for improving global agricultural policy, including overcoming problems of land degradation .

Barrett, C. B. Smallholder market participation: concepts and evidence from eastern and southern Africa. Food Policy 33 , 299–317 (2008).

Gollin, D. & Rogerson, R. Productivity, transport costs and subsistence agriculture. J. Dev. Econ. 107 , 38–48 (2014).

Barbier, E. B. Poverty, development and environment. Environ. Dev. Econ. 15 , 635–660 (2010). Highlights the causes and consequences of a potential poverty–environment ‘downward spiral’; consistently one of the most read articles in Environment and Development Economics .

Ahmed, A. U., Vargas Hill, R. & Naeem, F. in Marginality: Addressing the Nexus of Poverty, Exclusion and Ecology (eds von Braun, J. & Gatzweiler, F. W.) 85–99 (Springer, Berlin, 2014).

Banerjee, A. V. & Duflo., E. The economic lives of the poor. J. Econ. Persp. 21 , 141–168 (2007).

Lee, D. R. Agricultural sustainability and technology adoption: issues and policies for developing countries. Am. J. Agric. Econ. 87 , 1325–1334 (2005).

de Graaf, J. et al. Factors influencing adoption and continued use of long-term soil and water conservation measures in five developing countries. Appl. Geogr. 28 , 271–280 (2008).

Shiferaw, B. A., Okello, J. & Reddy, R. V. Adoption and adaptation of natural resource management innovations in smallholder agriculture: Reflections on key lessons and best practices. Environ. Dev. Sustain. 11 , 601–619 (2009).

Kassie, M., Teklewold, H., Jaleta, M., Marenya, P. & Erenstein, O. Understanding the adoption of a portfolio of sustainable intensification practices in eastern and southern Africa. Land Use Policy 42 , 400–411 (2015).

Debela, B., Shively, G., Angelsen, A. & Wik, M. Economic shocks, diversification, and forest use in Uganda. Land Econ. 88 , 139–154 (2012).

López-Feldman, A. Shocks, income and wealth: do they affect the extraction of natural resources by households? World Dev. 64 , S91–S100 (2014).

Narain, U., Gupta, S. & van‘t Veld, K. Poverty and resource dependence in rural India. Ecol. Econ. 66 , 161–176 (2008).

Robinson, E. J. Z. Resource-dependent livelihoods and the natural resource base. Annu. Rev. Resour. Econ. 8 , 281–301 (2016). A comprehensive review of the empirical evidence of how poor rural households depend on the surrounding natural environment .

Wunder, S., Börner, J., Shively, G. & Wyman, M. Safety nets, gap filling and forests: a global-comparative perspective. World Dev. 64 , S29–S42 (2014). A landmark and unique comparative study of uses of the surrounding natural environment by rural households and communities worldwide .

Pingali, P., Schneider, K. & Zurek, M. in Marginality: Addressing the Nexus of Poverty, Exclusion and Ecology (eds von Braun, J. & Gatzweiler, F. W.) 151–168 (Springer, Berlin, 2014).

Barrett, C. B., Garg, T. & McBride, L. Well-being dynamics and poverty traps. Annu. Rev. Resour. Econ. 8 , 303–327 (2016).

Carter, M. R. & Barrett, C. B. The economics of poverty traps and persistent poverty: an asset-based approach. J. Dev. Stud. 42 , 178–199 (2006). A novel and much-cited analysis of the critical role of rural household assets for poverty traps .

Shively, G. E. & Fisher, M. Smallholder labor and deforestation: a systems approach. Am. J. Agric. Econ. 86 , 1361–1366 (2004).

Pascual, U. & Barbier, E. B. On price liberalization, poverty, and shifting cultivation: an example from Mexico. Land Econ. 83 , 192–216 (2007).

Jansen, H. G. P. et al. Determinants of income-earning strategies and adoption of conservation practices in hillside communities in rural Honduras. Agric. Syst. 88 , 92–110 (2006).

Holden, S., Shiferaw, B. & Pender, J. Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands. Food Policy 29 , 369–392 (2004).

Takasaki, Y., Barham, B. L. & Coomes, O. T. Risk coping strategies in tropical forests: floods, illness, and resource extraction. Env. Dev. Econ. 9 , 203–224 (2004).

Angelsen, A. et al. Environmental income and rural livelihoods: a global-comparative analysis. World Dev. 64 , S12–S26 (2014).

Carter, M. R., Little, P. D., Mogues, T. & Negatu, W. Poverty traps and natural disasters in Ethiopia and Honduras. World Dev. 35 , 835–856 (2007).

Hallegatte, S. et al. Shock Waves: Managing the Impacts of Climate Change on Poverty (World Bank, 2015). A comprehensive and path-breaking global analysis of the impacts of climate change on the livelihoods of the poor .

McSweeney, K. Natural insurance, forest access, and compound misfortune: forest resources in smallholder coping strategies before and after Hurricane Mitch in northeastern Honduras. World Dev. 33 , 1453–1471 (2005).

Vedeld, P., Angelsen, A., Bojö, J., Sjaastad, E. & Kobugabe Berg, G. Forest environmental incomes and the rural poor. Forest Policy Econ. 9 , 869–879 (2007).

Battacharya, H. & Innes, R. Income and the environment in rural India: Is there a poverty trap? Am. J. Agric. Econ. 95 , 42–69 (2013).

Coxhead, I., Shively, G. E. & Shuai, X. Development policies, resource constraints, and agricultural expansion on the Philippine land frontier. Environ. Dev. Econ. 7 , 341–364 (2002).

Gonazález-Vega, C., Rodríguez-Meza, J., Southgate, D. & Maldonado, J. H. Poverty, structural transformation, and land use in El Salvador: learning from household panel data. Am. J. Agric. Econ. 86 , 1367–1374 (2004).

Deininger, K. & Jin, S. Tenure security and land-related investment: evidence from Ethiopia. Eur. Econ. Rev. 50 , 1245–1277 (2006).

Deininger, K., Jin, S. & Yadav, V. Does sharecropping affect long-term investment? Evidence from West Bengal’s tenancy reforms. Am. J. Agric. Econ. 95 , 772–790 (2013).

Gebremedhin, B. & Swinton, S. M. Investment in soil conservation in northern Ethiopia: the role of land tenure security and public programs. Agric. Econ. 29 , 69–84 (2003).

Besley, T. Property rights and investment incentives: theory and evidence from Ghana. J. Polit. Econ. 103 , 903–937 (1995).

Kabubo-Mariara, J. Land conservation and tenure security in Kenya: Boserup’s hypothesis revisited. Ecol. Econ. 64 , 25–35 (2007).

Eskander, S. M. S. U. & Barbier, E. B. Tenure security, human capital and soil conservation in an overlapping generation rural economy. Ecol. Econ. 135 , 176–185 (2017).

Place, F. Land tenure and agricultural productivity in Africa: a comparative analysis of the economics literature and recent policy strategies and reforms. World Dev. 37 , 1326–1336 (2009).

Holden, S. & Otsuka, K. The roles of land tenure reforms and land markets in the context of population growth and land use intensification in Africa. Food Policy 48 , 88–97 (2014).

Binswanger, H. P. & Sillers, D. A. Risk aversion and credit constraints in farmers’ decision-making: a reinterpretation. J. Dev. Stud. 22 , 504–539 (1983).

Boucher, S. & Guirkinger, C. Risk, wealth, and sectoral choice in rural credit markets. Am. J. Agric. Econ. 89 , 991–1004 (2007).

Zeller, M., Schreider, G., von Braun, J. & Heidhues, F. Rural Finance for Food Security for the Poor (International Food Policy Research Institute, 1997).

Barbier, E. B., López, R. E. & Hochard, J. P. Debt, poverty and resource management in a rural smallholder economy. Environ. Resource Econ. 65 , 411–427 (2016). Recipient of the 2017 European Association of Environmental and Resource Economists Award for outstanding publication in the journal Environmental and Resource Economics .

Barbier, E. B. Links between economic liberalization and rural resource degradation in the developing regions. Agric. Econ. 23 , 299–310 (2000).

López, R. E. Environmental externalities in traditional agriculture and the impact of trade liberalization: the case of Ghana. J. Dev. Econ. 53 , 17–39 (1997).

Barbier, E. B. The economic linkages between rural poverty and land degradation: some evidence from Africa. Agric. Ecosyst. Environ. 82 , 355–370 (2000).

López, R. & Galinato, G. I. Should governments stop subsidies to private goods? Evidence from rural Latin America. J. Public Econ. 91 , 1071–1094 (2007).

Massey, D. S., Axinn, W. G. & Ghimire, D. J. Environmental change and out-migration: evidence from Nepal. Popul. Environ . 32 , 100–136

Gray, C. L. & Bilsborrow, R. Environmental influences on human migration in rural Ecuador. Demography 50 , 1217–1241 (2013).

Gray, C. L. Soil quality and human migration in Kenya and Uganda. Global Environ. Change 21 , 421–430 (2011).

Shah, A. Land degradation and migration in a dry land region in India: extent, nature and determinants. Environ. Dev. Econ. 15 , 173–196 (2009).

Krieger, T. & Meirrieks, D. Land grabbing and ethnic conflict. Homo Oeconomicus 33 , 243–260

Dell’Angelo, J., D’Odorico, P., Rulli, M. C. & Marchand, P. The tragedy of the grabbed commons: Coercion and dispossession in the global land rush. World Dev. 92 , 1–12 (2017).

Holmén, H. Is land grabbing always what it is supposed to be? Large-scale land investments in sub-Saharan Africa. Dev. Policy Rev. 33 , 457–478 (2015).

De Schutter, O. How not to think of land grabbing: three critiques of large-scale investments in farmland. J. Peasant Stud. 38 , 249–279 (2011).

Davis, K. F., D’Odorico, P. & Rulli, C. Land grabbing: a preliminary quantification of economic impacts on rural livelihoods. Popul. Environ. 36 , 180–192 (2014).

Pender, J. & Hazell, P. in Promoting Sustainable Development in Less-Favored Areas (eds Pender, J & Hazell, P) (2020 Vision Initiative, Policy Brief Series, Focus 4, International Food Policy Research Institute 2000).

Fan, S. & Chan-Kang, C. Returns to investment in less-favoured areas in developing countries: a synthesis of evidence and implications for Africa. Food Policy 29 , 431–444 (2004).

Fan, S. & Hazell, P. Returns to public investment in the less-favored areas of India and China. Am. J. Agric. Econ. 83 , 1217–1222 (2001).

World Development Report 2008: Agricultural Development (World Bank, 2008).

The Global Land Outlook 1st ed. (UNCCD, 2017). The first comprehensive assessment of the state of global land resources, and the policy challenges for managing increasing desertification and land degradation .

The State of Food and Agriculture. Leveraging Food Systems for Inclusive Rural Transformation (FAO, 2017).

de Graff, J., Aklilu, A., Ouessar, M., Asins-Velis, S. & Kessler, A. The development of soil and water conservation policies and practices in five selected countries from 1960 to 2010. Land Use Policy 32 , 165–174 (2013).

Emran, M. S. & Hou, Z. Access to markets and rural poverty: evidence from household consumption in China. Rev. Econ. Stat. 95 , 682–697 (2013).

Haggblade, S., Hazell, P. & Reardon, T. The rural non-farm economy: prospects for growth and poverty reduction. World Dev. 38 , 1429–1441 (2010).

Andersson, E., Brogaard, S. & Olsson, L. The political ecology of land degradation. Annu. Rev. Environ. Resour. 36 , 295–319 (2011).

Pattanayak, S. K., Wunder, S. & Ferraro, P. J. Show me the money: do payments supply environmental services in developing countries? Rev. Environ. Econ. Policy 4 , 254–274 (2010).

Download references

Acknowledgements

The authors acknowledge the departments and institutions at Colorado State University and East Carolina University that supported this research. The authors are grateful to A. Grainger for invaluable advice and comments on this research and work.

Author information

Authors and affiliations.

Department of Economics and Senior Scholar, School of Global Environmental Sustainability, Colorado State University, Fort Collins, CO, USA

Edward B. Barbier

Department of Economics and UNC Coastal Studies, East Carolina University, Greenville, NC, USA

Jacob P. Hochard

You can also search for this author in PubMed   Google Scholar

Contributions

Both authors contributed to the development of the paper. E.B.B. led the initial planning, research and writing. J.P.H. undertook the spatial analysis.

Corresponding author

Correspondence to Edward B. Barbier .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Methods, Data and References

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Barbier, E.B., Hochard, J.P. Land degradation and poverty. Nat Sustain 1 , 623–631 (2018). https://doi.org/10.1038/s41893-018-0155-4

Download citation

Received : 21 June 2017

Accepted : 17 September 2018

Published : 13 November 2018

Issue Date : November 2018

DOI : https://doi.org/10.1038/s41893-018-0155-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Structural dynamics and sustainability in the agricultural sector: the case of the european union.

• Rasa Melnikiene
• Giulio Paolo Agnusdei

Agricultural and Food Economics (2024)

Nature-based Solutions can help restore degraded grasslands and increase carbon sequestration in the Tibetan Plateau

• Yingxin Wang

Communications Earth & Environment (2024)

A unifying modelling of multiple land degradation pathways in Europe

• Remus Prăvălie
• Pasquale Borrelli
• Marius-Victor Birsan

Nature Communications (2024)

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data

• Mingli Zang
• Xiaodong Wang
• Seyedeh Ensieh Faramarzi

Environmental Monitoring and Assessment (2024)

Community perspectives on the effectiveness of watershed management institutions in the Himalayas

• Nisha Silwal
• Nabin Dhungana
• Chun-Hung Lee

Journal of Mountain Science (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

• A-Z Publications

Annual Review of Environment and Resources

Volume 46, 2021, review article, open access, restoring degraded lands.

• Almut Arneth 1,2 , Lennart Olsson 3 , Annette Cowie 4,5 , Karl-Heinz Erb 6 , Margot Hurlbert 7 , Werner A. Kurz 8 , Alisher Mirzabaev 9 , and Mark D.A. Rounsevell 1,2,10
• View Affiliations Hide Affiliations Affiliations: 1 Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany; email: [email protected] [email protected] 2 Institute of Geography and Geo-ecology, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany 3 Lund University Centre for Sustainability Studies, Lund University, SE-221 00 Lund, Sweden; email: [email protected] 4 New South Wales (NSW) Department of Primary Industries Armidale Livestock Industries Centre, Armidale, New South Wales 2350, Australia; email: [email protected] 5 School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia 6 Institute of Social Ecology, University of Natural Resources and Life Sciences Vienna, 1070 Vienna, Austria; email: [email protected] 7 Johnson-Shoyama Graduate School of Public Policy, University of Regina, Regina, Saskatchewan S7N 5B8, Canada; email: [email protected] 8 Natural Resources Canada, Canadian Forest Service, Victoria, British Columbia V8Z 1M5, Canada; email: [email protected] 9 Center for Development Research, University of Bonn, 53113 Bonn, Germany; email: [email protected] 10 School of GeoSciences, University of Edinburgh, Edinburgh EH8 9XP, United Kingdom
• Vol. 46:569-599 (Volume publication date October 2021) https://doi.org/10.1146/annurev-environ-012320-054809
• First published as a Review in Advance on August 20, 2021
• Copyright © 2021 by Annual Reviews. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information

Land degradation continues to be an enormous challenge to human societies, reducing food security, emitting greenhouse gases and aerosols, driving the loss of biodiversity, polluting water, and undermining a wide range of ecosystem services beyond food supply and water and climate regulation. Climate change will exacerbate several degradation processes. Investment in diverse restoration efforts, including sustainable agricultural and forest land management, as well as land set aside for conservation wherever possible, will generate co-benefits for climate change mitigation and adaptation and morebroadly for human and societal well-being and the economy. This review highlights the magnitude of the degradation problem and some of the key challenges for ecological restoration. There are biophysical as well as societal limits to restoration. Better integrating policies to jointly address poverty, land degradation, and greenhouse gas emissions and removals is fundamental to reducing many existing barriers and contributing to climate-resilient sustainable development.

Article metrics loading...

Full text loading...

Literature Cited

• 1.  Arneth A , Denton F , Agus F , Elbehri A , Erb K et al. 2019 . Framing and context. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems PR Shukla, J Skea, E Calvo Buendia, V Masson-Delmotte, HO Pörtner et al. 77– 129 Geneva: Intergov. Panel Clim. Change [Google Scholar]
• 2.  IPBES (Intergov. Sci.-Policy Platf. Biodivers. Ecosyst. Serv.) 2019 . The IPBES Global Assessment on Biodiversity and Ecosystem Services . Bonn, Ger: IPBES Secr. [Google Scholar]
• 3.  Olsson L , Barbosa H , Bhadwal S , Cowie A , Delusca K et al. 2019 . Land degradation. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems PR Shukla, J Skea, E Calvo Buendia, V Masson-Delmotte, HO Pörtner et al. 345– 436 Geneva: Intergov. Panel Clim. Change [Google Scholar]
• 4.  Mirzabaev A , Wu J , Evans J , García-Oliva F , Hussein IAG et al. 2019 . Desertification. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems PR Shukla, J Skea, E Calvo Buendia, V Masson-Delmotte, HO Pörtner et al. 249– 343 Geneva: Intergov. Panel Clim. Change [Google Scholar]
• 5.  Altieri AH , Harrison SB , Seemann J , Collin R , Diaz RJ , Knowlton N 2017 . Tropical dead zones and mass mortalities on coral reefs. PNAS 114 : 3660– 65 [Google Scholar]
• 6.  Haberl H , Erb K-H , Krausmann F. 2014 . Human appropriation of net primary production: patterns, trends, and planetary boundaries. Annu. Rev. Environ. Resour. 39 : 363– 91 [Google Scholar]
• 7.  Mottet A , de Haan C , Falcucci A , Tempio G , Opio C , Gerber PJ. 2017 . Livestock: On our plates or eating at our table? A new analysis of the feed/food debate. Glob. Food Secur. 14 : 1– 8 [Google Scholar]
• 8.  FAO. (United Nations Food and Agric. Organ.) 2020 . Global Forest Resources Assessment 2020: Main report Rome. Rep., FAO https://doi.org/10.4060/ca9825en [Crossref] [Google Scholar]
• 9.  Erb K-H , Kastner T , Plutzar C , Bais ALS , Carvalhais N et al. 2018 . Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553 : 73– 76 [Google Scholar]
• 10.  Marzen M , Iserloh T , de Lima J , Fister W , Ries JB. 2017 . Impact of severe rain storms on soil erosion: experimental evaluation of wind-driven rain and its implications for natural hazard management. Sci. Total Environ. 590 : 502– 13 [Google Scholar]
• 11.  IPBES (Intergov. Sci.-Policy Platf. Biodivers. Ecosyst. Serv.) 2018 . The IPBES Assessment Report on Land Degradation and Restoration Bonn, Ger: IPBES Secr. [Google Scholar]
• 12.  Anderegg WRL , Trugman AT , Badgley G , Anderson CM , Bartuska A et al. 2020 . Climate-driven risks to the climate mitigation potential of forests. Science 368 : eaaz7005 [Google Scholar]
• 13.  Hember RA , Kurz WA , Girardin MP. 2019 . Tree ring reconstructions of stemwood biomass indicate increases in the growth rate of black spruce trees across boreal forests of Canada. J. Geophys. Res. Biogeosci. 124 : 2460– 80 [Google Scholar]
• 14.  Trant A , Higgs E , Starzomski BM. 2020 . A century of high elevation ecosystem change in the Canadian Rocky Mountains. Sci. Rep. 10 : 9698 [Google Scholar]
• 15.  Bullock EL , Woodcock CE , Souza C , Olofsson P. 2020 . Satellite-based estimates reveal widespread forest degradation in the Amazon. Glob. Change Biol. 26 : 2956– 69 [Google Scholar]
• 16.  Matricardi EAT , Skole DL , Costa OB , Pedlowski MA , Samek JH , Miguel EP 2020 . Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369 : 1378– 82 [Google Scholar]
• 17.  Maxwell SL , Evans T , Watson JEM , Morel A , Grantham H et al. 2019 . Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5 : eaax2546 [Google Scholar]
• 18.  Pugh TAM , Lindeskog M , Smith B , Poulter B , Arneth A et al. 2019 . Role of forest regrowth in global carbon sink dynamics. PNAS 116 : 4382– 87 [Google Scholar]
• 19.  Mayer M , Prescott CE , Abaker WEA , Augusto L , Cecillon L et al. 2020 . Tamm Review: Influence of forest management activities on soil organic carbon stocks: a knowledge synthesis. Forest Ecol. Manag. 466 : 118127 [Google Scholar]
• 20.  Thorn S , Seibold S , Leverkus AB , Michler T , Müller J et al. 2020 . The living dead: acknowledging life after tree death to stop forest degradation. Front. Ecol. Environ. 18 : 505– 12 [Google Scholar]
• 21.  Meyfroidt P , Lambin EF. 2011 . Global forest transition: prospects for an end to deforestation. Annu. Rev. Environ. Resour. 36 : 343– 71 [Google Scholar]
• 22.  Koutroulis A. 2018 . Dryland changes under different levels of global warming. Sci. Total Environ. 655 : 482– 511 [Google Scholar]
• 23.  Prăvălie R. 2016 . Drylands extent and environmental issues. A global approach. Earth-Sci. Rev. 161 : 259– 78 [Google Scholar]
• 24.  FAO (United Nations Food Agric. Organ.) 2016 . Trees, Forests and Land Use in Drylands: The First Global Assessment—Preliminary Findings Rome: FAO [Google Scholar]
• 25.  Brandt M , Tucker CJ , Kariryaa A , Rasmussen K , Abel C et al. 2020 . An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587 : 78– 82 [Google Scholar]
• 26.  Murphy BP , Andersen AN , Parr CL. 2016 . The underestimated biodiversity of tropical grassy biomes. Philos. Trans. R. Soc. B 371 : 20150319 [Google Scholar]
• 27.  Carbutt C , Henwood WD , Gilfedder LA. 2017 . Global plight of native temperate grasslands: going, going, gone?. Biodivers. Conserv. 26 : 2911– 32 [Google Scholar]
• 28.  van Oijen M , Bellocchi G , Hoglind M. 2018 . Effects of climate change on grassland biodiversity and productivity: the need for a diversity of models. Agronomy 8 : 14 [Google Scholar]
• 29.  Brandt M , Mbow C , Diouf A , Verger A , Samimi C , Fensholt R. 2014 . Ground and satellite-based evidence of the biophysical mechanisms behind the greening Sahel. Glob. Change Biol. 21 : 1610– 20 [Google Scholar]
• 30.  Rishmawi K , Prince S 2016 . Environmental and anthropogenic degradation of vegetation in the Sahel from 1982 to 2006. Remote Sens . 8 : 948 [Google Scholar]
• 31.  Lehman CER , Parr CL. 2016 . Tropical grassy biomes: linking ecology, human use and conservation. Philos. Trans. R. Soc. B 371 : 20160329 [Google Scholar]
• 32.  Morton J. 2010 . Why should governmentality matter for the study of pastoral development?. Nomad. Peoples 14 : 6– 30 [Google Scholar]
• 33.  Ramankutty N , Evan AT , Monfreda C , Foley JA. 2008 . Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22 : GB1003 [Google Scholar]
• 34.  Gossner MM , Lewinsohn TM , Kahl T , Grassein F , Boch S et al. 2016 . Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540 : 266– 69 [Google Scholar]
• 35.  Shin Y-J , Arneth A , Roy-Chaudhury R , Midgley G , Boafo Y et al. 2019 . Plausible futures of nature, its contributions to people and their good quality of life. The IPBES Global Assessment on Biodiversity and Ecosystem Services IPBES (Intergov. Sci.-Policy Platf. Biodivers. Ecosyst. Serv.) Bonn, Ger: IPBES Secr https://ipbes.net/sites/default/files/ipbes_global_assessment_chapter_4_unedited_31may.pdf [Google Scholar]
• 36.  Gang C , Zhou W , Chen Y , Wang Z , Sun Z et al. 2014 . Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environ. Earth Sci. 72 : 4273– 82 [Google Scholar]
• 37.  McSherry ME , Ritchie ME. 2013 . Effects of grazing on grassland soil carbon: a global review. Glob. Change Biol. 19 : 1347– 57 [Google Scholar]
• 38.  Smith P. 2014 . Do grasslands act as a perpetual sink for carbon?. Glob. Change Biol. 20 : 2708– 11 [Google Scholar]
• 39.  Sanderman J , Hengl T , Fiske GJ 2017 . Soil carbon debt of 12,000 years of human land use. PNAS 114 : 9575– 80 [Google Scholar]
• 40.  Bossio DA , Cook-Patton SC , Ellis PW , Fargione J , Sanderman J et al. 2020 . The role of soil carbon in natural climate solutions. Nat. Sustain. 3 : 391– 98 [Google Scholar]
• 41.  Humphreys J , Brye KR , Rector C , Gbur EE. 2019 . Methane emissions from rice across a soil organic matter gradient in Alfisols of Arkansas, USA. Geoderma Reg . 16 : e00200 [Google Scholar]
• 42.  da Silva Cardoso A , Quintana BG , Janusckiewicz ER , de Figueiredo Brito L , da Silva Morgado E et al. 2019 . How do methane rates vary with soil moisture and compaction, N compound and rate, and dung addition in a tropical soil?. Int. J. Biometeorol. 63 : 1533– 40 [Google Scholar]
• 43.  Tian H , Yang J , Xu R , Lu C , Canadell JG et al. 2019 . Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: magnitude, attribution, and uncertainty. Glob. Change Biol. 25 : 640– 59 [Google Scholar]
• 44.  Kayranli B , Scholz M , Mustafa A , Hedmark Å. 2010 . Carbon storage and fluxes within freshwater wetlands: a critical review. Wetlands 30 : 111– 24 [Google Scholar]
• 45.  Page SE , Baird AJ. 2016 . Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41 : 35– 57 [Google Scholar]
• 46.  Tootchi A , Jost A , Ducharne A. 2019 . Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11 : 189– 220 [Google Scholar]
• 47.  Prince S , Von Maltitz G , Zhang F , Byrne K , Driscoll C et al. 2018 . Status and trends of land degradation and restoration and associated changes in biodiversity and ecosystem functions. The IPBES Assessment Report on Land Degradation and Restoration IPBES (Intergov. Sci.-Policy Platf. Biodivers. Ecosyst. Serv.), 315– 426 Bonn, Ger: IPBES Secr. [Google Scholar]
• 48.  Darrah SE , Shennan-Farpón Y , Loh J , Davidson NC , Finlayson CM et al. 2019 . Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Indic. 99 : 294– 98 [Google Scholar]
• 49.  Colloff MJ , Lavorel S , Wise RM , Dunlop M , Overton IC , Williams KJ. 2016 . Adaptation services of floodplains and wetlands under transformational climate change. Ecol. Appl. 26 : 1003– 17 [Google Scholar]
• 50.  Nisbet EG , Manning MR , Dlugokencky EJ , Fisher RE , Lowry D et al. 2019 . Very strong atmospheric methane growth in the 4 years 2014–2017: implications for the Paris Agreement. Glob. Biogeochem. Cycles 33 : 318– 42 [Google Scholar]
• 51.  Mikaloff Fletcher SE , Schaefer H 2019 . Rising methane: a new climate challenge. Science 364 : 932– 33 [Google Scholar]
• 52.  Oh Y , Zhuang Q , Liu L , Welp LR , Lau MCY et al. 2020 . Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic. Nat. Clim. Change 10 : 317– 21 [Google Scholar]
• 53.  Hemes KS , Chamberlain SD , Eichelmann E , Anthony T , Valach A et al. 2019 . Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands. Agric. Forest Meteorol. 268 : 202– 14 [Google Scholar]
• 54.  Meli P , Benayas J , Balvanera P , Martinez-Ramos M. 2014 . Restoration enhances wetland biodiversity and ecosystem service supply, but results are context-dependent: a meta-analysis. PLOS ONE 9 : e93507 [Google Scholar]
• 55.  Hogeboom RJ , de Bruin D , Schyns JF , Krol MS , Hoekstra AY. 2020 . Capping human water footprints in the world's river basins. Earths Future 8 : e2019EF001363 [Google Scholar]
• 56.  Bogardi JJ , Fekete BM , Vorosmarty CJ. 2013 . Planetary boundaries revisited: a view through the ‘water lens. ’. Curr. Opin. Environ. Sustain. 5 : 581– 89 [Google Scholar]
• 57.  Grill G , Lehner B , Thieme M , Geenen B , Tickner D et al. 2019 . Mapping the world's free-flowing rivers. Nature 569 : 215– 21 [Google Scholar]
• 58.  Sabater S , Bregoli F , Acuna V , Barcelo D , Elosegi A et al. 2018 . Effects of human-driven water stress on river ecosystems: a meta-analysis. Sci. Rep. 8 : 11462 [Google Scholar]
• 59.  Zarfl C , Berlekamp J , He F , Jähnig SC , Darwall W , Tockner K. 2019 . Future large hydropower dams impact global freshwater megafauna. Sci. Rep. 9 : 18531 [Google Scholar]
• 60.  Zhu ZC , Piao SL , Myneni RB , Huang MT , Zeng ZZ et al. 2016 . Greening of the Earth and its drivers. Nat. Clim. Change 6 : 791– 95 [Google Scholar]
• 61.  Liu YY , Yang Y , Wang Q , Khalifa M , Zhang ZY et al. 2019 . Assessing the dynamics of grassland net primary productivity in response to climate change at the global scale. Chinese Geogr. Sci. 29 : 725– 40 [Google Scholar]
• 62.  Le Quéré C , Andrew RM , Friedlingstein P , Sitch S , Hauck J et al. 2018 . Global carbon budget 2018. Earth Syst. Sci. Data 10 : 2141– 94 [Google Scholar]
• 63.  Smith P , Calvin K , Nkem J , Campbell D , Cherubini F et al. 2019 . Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification?. Glob. Change Biol. 26 : 1532– 75 [Google Scholar]
• 64.  Wilhite D , Pulwarty RS. 2017 . Drought and Water Crises, Integrating Science, Management, and Policy Boca Raton: CRC Press [Google Scholar]
• 65.  Mo K , Lettenmaier D. 2015 . Heat wave flash droughts in decline. Geophys. Res. Lett. 42 : 2823– 29 [Google Scholar]
• 66.  Stroosnijder L. 2009 . Modifying land management in order to improve efficiency of rainwater use in the African highlands. Soil Tillage Res . 103 : 247– 56 [Google Scholar]
• 67.  Jia G , Shevliakova E , Artaxo P , De Noblet-Ducoudré N , Houghton R et al. 2019 . Land–climate interactions. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems PR Shukla, J Skea, E Calvo Buendia, V Masson-Delmotte, HO Pörtner et al. 131– 247 Geneva: Intergov. Panel Clim. Change [Google Scholar]
• 68.  Zheng J , Yingzhuo Y , Zhang X , Hao Z. 2018 . Variation of extreme drought and flood in North China revealed by document-based seasonal precipitation reconstruction for the past 300 years. Clim. Past 14 : 1135– 45 [Google Scholar]
• 69.  Ziese M , Schneider U , Meyer-Christoffer A , Schamm K , Vido J et al. 2014 . The GPCC Drought Index—a new, combined and gridded global drought index. Earth Syst. Sci. Data 6 : 285– 95 [Google Scholar]
• 70.  Byers E , Gidden M , Leclère D , Balkovič J , Burek P et al. 2018 . Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett. 13 : 5 [Google Scholar]
• 71.  Hurlbert M , Krishnaswamy J , Davin E , Johnson FX , Mena CF et al. 2019 . Risk management and decision-making in relation to sustainable development. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems PR Shukla, J Skea, E Calvo Buendia, V Masson-Delmotte, HO Pörtner et al. 673– 800 Geneva: Intergov. Panel Clim. Change [Google Scholar]
• 72.  Shrestha S , Hoang NAT , Shrestha PK , Bhatta B. 2018 . Climate change impact on groundwater recharge and suggested adaptation strategies for selected Asian cities. APN Sci. Bull. 8 : 1 https://doi.org/10.30852/sb.2018.499 [Crossref] [Google Scholar]
• 73.  Clarke H , Evans JP. 2018 . Exploring the future change space for fire weather in southeast Australia. Theor. Appl. Climatol. 136 : 513– 27 [Google Scholar]
• 74.  Williams AP , Allen CD , Millar CI , Swetnam TW , Michaelsen J et al. 2010 . Forest responses to increasing aridity and warmth in the southwestern United States. PNAS 107 : 21289– 94 [Google Scholar]
• 75.  McLauchlan KK , Higuera PE , Miesel J , Rogers BM , Schweitzer J et al. 2020 . Fire as a fundamental ecological process: research advances and frontiers. J. Ecol. 108 : 2047– 69 [Google Scholar]
• 76.  Ward M , Tulloch AIT , Radford JQ , Williams BA , Reside AE et al. 2020 . Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4 : 1321– 26 [Google Scholar]
• 77.  Halofsky JE , Peterson DL , Harvey BJ. 2020 . Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecol 16 : 4 [Google Scholar]
• 78.  Kukavskaya EA , Buryak LV , Shvetsov EG , Conard SG , Kalenskaya OP. 2016 . The impact of increasing fire frequency on forest transformations in southern Siberia. Forest Ecol. Manag. 382 : 225– 35 [Google Scholar]
• 79.  Seidl R , Thom D , Kautz M , Martin-Benito D , Peltoniemi M et al. 2017 . Forest disturbances under climate change. Nat. Clim. Change 7 : 395– 402 [Google Scholar]
• 80.  Pellegrini AFA , Hobbie SE , Reich PB , Jumpponen A , Brookshire ENJ et al. 2020 . Repeated fire shifts carbon and nitrogen cycling by changing plant inputs and soil decomposition across ecosystems. Ecol. Monogr. 90 : e01409 [Google Scholar]
• 81.  Knorr K , Jiang L , Arneth A. 2016 . Climate, CO 2 , and demographic impacts on global wildfire emissions. Biogeosciences 13 : 267– 82 [Google Scholar]
• 82.  Donat MG , Lowry AL , Alexander LV , O'Gorman PA , Maher N. 2016 . More extreme precipitation in the world's dry and wet regions. Nat. Clim. Change 6 : 508– 13 [Google Scholar]
• 83.  Van Der Bolt B , Van Nes EH , Bathiany S , Vollebregt ME , Scheffer M. 2018 . Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8 : 478– 84 [Google Scholar]
• 84.  Wang Z-H , Li S-X. 2019 . Nitrate N loss by leaching and surface runoff in agricultural land: a global issue (a review). Advances in Agronomy , Vol. 156 DL Sparks 159– 217 Amsterdam: Elsevier [Google Scholar]
• 85.  Withers P , Neal C , Jarvie H , Doody D. 2014 . Agriculture and eutrophication: Where do we go from here?. Sustainability 6 : 5853– 75 [Google Scholar]
• 86.  Eekhout JP , De Vente J. 2020 . How soil erosion model conceptualization affects soil loss projections under climate change. Prog. Phys. Geogr. Earth Environ. 44 : 212– 32 [Google Scholar]
• 87.  Lee C-Y , Camargo SJ , Sobel AH , Tippett MK. 2020 . Statistical–dynamical downscaling projections of tropical cyclone activity in a warming climate: two diverging genesis scenarios. J. Clim. 33 : 4815– 34 [Google Scholar]
• 88.  Patricola CM , Wehner MF. 2018 . Anthropogenic influences on major tropical cyclone events. Nature 563 : 339– 46 [Google Scholar]
• 89.  Marsooli R , Lin N , Emanuel K , Feng K. 2019 . Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns. Nat. Commun. 10 : 3785 [Google Scholar]
• 90.  Aksha SK , Juran L , Resler LM. 2018 . Spatial and temporal analysis of natural hazard mortality in Nepal. Environ. Hazards 17 : 163– 79 [Google Scholar]
• 91.  IPCC (Intergov. Panel Clim. Change) 2018 . Global Warming of 1.5°C Geneva: IPCC [Google Scholar]
• 92.  Hanssen SV , Daioglou V , Steinmann ZJN , Doelman JC , Van Vuuren DP , Huijbregts MAJ. 2020 . The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change 10 : 1023– 29 [Google Scholar]
• 93.  Gregg JS , Izaurralde RC. 2010 . Effect of crop residue harvest on long-term crop yield, soil erosion and nutrient balance: trade-offs for a sustainable bioenergy feedstock. Biofuels 1 : 69– 83 [Google Scholar]
• 94.  Liska AJ , Yang H , Milner M , Goddard S , Blanco-Canqui H et al. 2014 . Biofuels from crop residue can reduce soil carbon and increase CO 2 emissions. Nat. Clim. Change 4 : 398– 401 [Google Scholar]
• 95.  Hof C , Voskamp A , Biber MF , Böhning-Gaese K , Engelhardt EK et al. 2018 . Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. PNAS 115 : 13294– 99 [Google Scholar]
• 96.  Fuss S , Lamb WF , Callaghan MW , Hilaire J , Creutzig F et al. 2018 . Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett. 13 : 063002 [Google Scholar]
• 97.  Girardello M , Santangeli A , Mori E , Chapman A , Fattorini S et al. 2019 . Global synergies and trade-offs between multiple dimensions of biodiversity and ecosystem services. Sci. Rep. 9 : 5636 [Google Scholar]
• 98.  Strassburg BBN , Beyer HL , Crouzeilles R , Iribarrem A , Barros P et al. 2019 . Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3 : 62– 70 [Google Scholar]
• 99.  Palomo I , Dujardin Y , Midler E , Robin M , Sanz MJ , Pascual U 2019 . Modeling trade-offs across carbon sequestration, biodiversity conservation, and equity in the distribution of global REDD plus funds. PNAS 116 : 22645– 50 [Google Scholar]
• 100.  Nilsson C , Riis T , Sarneel J , Svavarsdóttir K. 2018 . Ecological restoration as a means of managing inland flood hazards. BioScience 68 : 89– 99 [Google Scholar]
• 101.  Morecroft MD , Duffield S , Harley M , Pearce-Higgins JW , Stevens N et al. 2019 . Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366 : eaaw9256 [Google Scholar]
• 102.  Donatti CI , Harvey CA , Hole D , Panfil SN , Schurman H. 2020 . Indicators to measure the climate change adaptation outcomes of ecosystem-based adaptation. Clim. Change 158 : 413– 33 [Google Scholar]
• 103.  Seddon N , Chausson A , Berry P , Girardin CAJ , Smith A , Turner B. 2020 . Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. B 375 : 20190120 [Google Scholar]
• 104.  Rhodes CJ. 2017 . The imperative for regenerative agriculture. Sci. Prog. 100 : 80– 129 [Google Scholar]
• 105.  Montgomery DR. 2017 . Growing a Revolution: Bringing Our Soil Back to Life New York: WW Norton & Co. [Google Scholar]
• 106.  Kassam A , Friedrich T , Derpsch R. 2019 . Global spread of conservation agriculture. Int. J. Environ. Stud. 76 : 29– 51 [Google Scholar]
• 107.  Davis SC , Boddey RM , Alves BJR , Cowie AL , George BH et al. 2013 . Management swing potential for bioenergy crops. GCB Bioenergy 5 : 623– 38 [Google Scholar]
• 108.  Witters N , Van Slycken S , Ruttens A , Adriaensen K , Meers E et al. 2009 . Short-rotation coppice of willow for phytoremediation of a metal-contaminated agricultural area: a sustainability assessment. BioEnergy Res 2 : 144– 52 [Google Scholar]
• 109.  Immerzeel DJ , Verweij PA , van der Hilst F , Faaij APC. 2014 . Biodiversity impacts of bioenergy crop production: a state-of-the-art review. GCB Bioenergy 6 : 189– 209 [Google Scholar]
• 110.  De Stefano A , Jacobson MG. 2018 . Soil carbon sequestration in agroforestry systems: a meta-analysis. Agroforestry Syst 92 : 285– 99 [Google Scholar]
• 111.  Wilson MH , Lovell ST. 2016 . Agroforestry—the next step in sustainable and resilient agriculture. Sustainability 8 : 574 [Google Scholar]
• 112.  Churkina G , Organschi A , Reyer CPO , Ruff A , Vinke K et al. 2020 . Buildings as a global carbon sink. Nat. Sustain. 3 : 269– 76 [Google Scholar]
• 113.  Veldman JW , Overbeck GE , Negreiros D , Mahy G , Le Stradic S et al. 2015 . Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65 : 1011– 18 [Google Scholar]
• 114.  Abreu RCR , Hoffmann WA , Vasconcelos HL , Pilon NA , Rossatto DR , Durigan G. 2017 . The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 3 : e1701284 [Google Scholar]
• 115.  Brundu G , Richardson DM 2016 . Planted forests and invasive alien trees in Europe: a code for managing existing and future plantings to mitigate the risk of negative impacts from invasions. NeoBiota 30 : 5– 47 [Google Scholar]
• 116.  Wilson SJ , Schelhas J , Grau R , Nanni AS , Sloan S. 2017 . Forest ecosystem-service transitions: the ecological dimensions of the forest transition. Ecol. Soc. 22 : 38 [Google Scholar]
• 117.  Aerts R , Honnay O. 2011 . Forest restoration, biodiversity and ecosystem functioning. BMC Ecol 11 : 29 [Google Scholar]
• 118.  Liang JJ , Crowther TW , Picard N , Wiser S , Zhou M et al. 2016 . Positive biodiversity-productivity relationship predominant in global forests. Science 354 : aaf8957 [Google Scholar]
• 119.  Crouzeilles R , Curran M , Ferreira MS , Lindenmayer DB , Grelle CEV , Benayas JMR. 2016 . A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun. 7 : 11666 [Google Scholar]
• 120.  Cross SL , Bateman PW , Cross AT. 2020 . Restoration goals: Why are fauna still overlooked in the process of recovering functioning ecosystems and what can be done about it?. Ecol. Manag. Restor. 21 : 4– 8 [Google Scholar]
• 121.  Jouquet P , Blanchart E , Capowiez Y. 2014 . Utilization of earthworms and termites for the restoration of ecosystem functioning. Appl. Soil Ecol. 73 : 34– 40 [Google Scholar]
• 122.  Schmitz OJ , Wilmers CC , Leroux SJ , Doughty CE , Atwood TB et al. 2018 . Animals and the zoogeochemistry of the carbon cycle. Science 362 : eaar3213 [Google Scholar]
• 123.  Kimmerer R 2011 . Restoration and reciprocity: the contributions of traditional ecological knowledge. Human Dimensions of Ecological Restoration . Society for Ecological Restoration , ed . D Egan, EE Hjerpe, J Abrams 257– 76 Washington, DC: Island Press [Google Scholar]
• 124.  Nkonya E , Mirzabaev A , von Braun J 2016 . Economics of land degradation and improvement: an introduction and overview. Economics of Land Degradation and Improvement — A Global Assessment for Sustainable Development E Nkonya, A Mirzabaev, J von Braun 1– 14 Cham, Switz: Springer Int. Publ. [Google Scholar]
• 125.  Mentis M. 2020 . Environmental rehabilitation of damaged land. Forest Ecosyst 7 : 19 [Google Scholar]
• 126.  Giger M , Liniger H , Sauter C , Schwilch G. 2018 . Economic benefits and costs of sustainable land management technologies: an analysis of WOCAT's global data. Land Degrad. Dev. 29 : 962– 74 [Google Scholar]
• 127.  Lambin E , Meyfroidt P , Rueda X , Blackman A , Börner J et al. 2014 . Effectiveness and synergies of policy instruments for land use governance in tropical regions. Glob. Environ. Change 28 : 129– 40 [Google Scholar]
• 128.  Reed MS , Stringer LC , Dougill AJ , Perkins JS , Atlhopheng JR et al. 2015 . Reorienting land degradation towards sustainable land management: linking sustainable livelihoods with ecosystem services in rangeland systems. J. Environ. Manag. 151 : 472– 85 [Google Scholar]
• 129.  Scown MW , Brady MV , Nicholas KA. 2020 . Billions in misspent EU agricultural subsidies could support the Sustainable Development Goals. One Earth 3 : 237– 50 [Google Scholar]
• 130.  Wolff S , Schrammeijer EA , Schulp C , Verburg PH. 2018 . Meeting global land restoration and protection targets: What would the world look like in 2050?. Glob. Environ. Change 52 : 259– 72 [Google Scholar]
• 131.  Metzger JP , Esler K , Krug C , Arias M , Tambosi L et al. 2017 . Best practice for the use of scenarios for restoration planning. Curr. Opin. Environ. Sustain. 29 : 14– 25 [Google Scholar]
• 132.  Folke C. 2016 . Resilience (Republished). Ecol . Soc 21 : 44 [Google Scholar]
• 133.  Acosta LA , Virk A , Kumar R , Sharma S , Ikeda T et al. 2018 . Options for governance and decision-making across scales and sectors. The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Asia and the Pacific M Karki, SS Sellamuttu, W Suzuki, S Okayasu 429– 536 Bonn, Ger: IPBES Secr. [Google Scholar]
• 134.  George C , Reed MG. 2015 . Operationalising just sustainability: towards a model for place-based governance. Local Environ 22 : 1105– 23 [Google Scholar]
• 135.  Parlee CE , Wiber MG. 2018 . Using conflict over risk management in the marine environment to strengthen measures of governance. Ecol. Soc. 23 : 5 [Google Scholar]
• 136.  Cowie AL , Orr BJ , Castillo Sanchez VM , Chasek P , Crossman ND et al. 2018 . Land in balance: the scientific conceptual framework for land degradation neutrality. Environ. Sci. Policy 79 : 25– 35 [Google Scholar]
• 137.  Fagan ME , Reid JL , Holland MB , Drew JG , Zahawi RA. 2020 . How feasible are global forest restoration commitments?. Conserv. Lett. 13 : e12700 [Google Scholar]
• 138.  Seddon N , Sengupta S , García-Espinosa M , Hauler I , Herr D , Rizvi AR. 2019 . Nature-Based Solutions in Nationally Determined Contributions: Synthesis and Recommendations for Enhancing Climate Ambition and Action by 2020 Gland, Switz./Oxford: Int. Union Conserv. Nat., Univ. Oxford [Google Scholar]
• 139.  Metzger MJ , Dick J , Gardner A , Bellamy C , Blackstock K et al. 2019 . Knowledge sharing, problem solving and professional development in a Scottish Ecosystem Services Community of Practice. Reg. Environ . Change 19 : 2275– 86 [Google Scholar]
• 140.  Eakin H , York A , Aggarwal R , Waters S , Welch J et al. 2016 . Cognitive and institutional influences on farmers’ adaptive capacity: insights into barriers and opportunities for transformative change in central Arizona. Reg. Environ. Change 16 : 801– 14 [Google Scholar]
• 141.  Wreford A , Ignaciuk A , Gruere G. 2017 . Overcoming barriers to the adoption of climate-friendly practices in agriculture . Pap. 101 Food, Agric. Fish., Org. Econ. Co-op. Dev. Paris: [Google Scholar]
• 142.  Barnett J , Evans LS , Gross C , Kiem AS , Kingsford RT et al. 2015 . From barriers to limits to climate change adaptation: path dependency and the speed of change. Ecol. Soc . 20 : 5 [Google Scholar]
• 143.  Dow K , Berkhout F , Preston BL , Klein RJT , Midgley G , Shaw MR. 2013 . Limits to adaptation. Nat. Clim. Change 3 : 305– 7 [Google Scholar]
• 144.  Arneth A , Shin Y-J , Leadley P , Rondinini C , Bukvareva E et al. 2020 . Post-2020 biodiversity targets need to embrace climate change. PNAS 117 : 30882– 91 [Google Scholar]
• 145.  Kwakkel JH , Haasnoot M , Walker WE. 2016 . Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. Environ. Model. Software 86 : 168– 83 [Google Scholar]
• 146.  Otto IM , Wiedermann M , Cremades R , Donges JF , Auer C , Lucht W. 2020 . Human agency in the Anthropocene. Ecol. Econ. 167 : 106463 [Google Scholar]
• 147.  Pörtner HO , Scholes RJ , Agard J , Archer E , Arneth A et al. 2021 . Scientific outcome of the IPBES-IPCC co-sponsored workshop on biodiversity and climate change Rep., IPBES (Intergov. Sci.-Policy Platf. Biodivers. Ecosyst. Serv.) Bonn, Ger: http://doi.org/10.5281/zenodo.4659158 [Crossref] [Google Scholar]
• 148.  Bullock JM , Aronson J , Newton AC , Pywell RF , Rey-Benayas JM. 2011 . Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26 : 541– 49 [Google Scholar]
• 149.  Kollmann J , Meyer ST , Bateman R , Conradi T , Gossner MM et al. 2016 . Integrating ecosystem functions into restoration ecology—recent advances and future directions. Restor. Ecol. 24 : 722– 30 [Google Scholar]

Data & Media loading...

• Article Type: Review Article

Most Read This Month

Most cited most cited rss feed, adaptive governance of social-ecological systems, dynamics of land-use and land-cover change in tropical regions, climate change and food systems, wetland resources: status, trends, ecosystem services, and restorability, global water pollution and human health, adaptation to environmental change: contributions of a resilience framework, i ndustrial s ymbiosis : literature and taxonomy, environmental governance, c arbon d ioxide e missions from the g lobal c ement i ndustry 1, global biogeochemical cycling of mercury: a review.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

A bibliometric analysis on land degradation: current status, development, and future directions.

1. Introduction

2. data sources and research methods, 2.1. data sources, 2.2. research method, 3. results analysis, 3.1. distribution of annual documents, 3.2. analysis of cited papers in land degradation research, 3.2.1. analysis of the annual development trend of citations, 3.2.2. historical analysis of citied papers of land-degradation research, 3.3. analysis of main researcher, 3.4. analysis of distribution characteristics of major research countries/regions, 3.5. analysis of keywords, 3.5.1. analysis of high-frequency keywords, 3.5.2. cluster analysis and multiple correspondence analysis of high-frequency keywords, 3.6. evolution analysis of themes in the field of land degradation, 4. discussion and conclusions, 4.1. discussion, 4.2. conclusions, author contributions, acknowledgments, conflicts of interest.

Click here to enlarge figure

• Sun, T.; Feng, Z.; Yang, Y.; Lin, Y.; Wu, Y. Research on land resource carrying capacity: Progress and prospects. J. Res. Ecol. 2018 , 9 , 331–340. [ Google Scholar ]
• Hammad, A.; Tumeizi, A. Land degradation: Socioeconomic and environmental causes and consequences in the eastern Mediterranean. Land Degrad. Dev. 2012 , 23 , 216–226. [ Google Scholar ] [ CrossRef ]
• Batunacun; Wieland, R.; Lakes, T.; Yunfeng, H.; Nendel, C. Identifying drivers of land degradation in Xilingol, China, between 1975 and 2015. Land Use Pol. 2019 , 83 , 543–559. [ Google Scholar ] [ CrossRef ]
• Reed, M.S.; Buenemann, M.; Atlhopheng, J.; Akhtar-Schuster, M.; Bachmann, F.; Bastin, G.; Bigas, H.; Chanda, R.; Dougill, A.J.; Essahli, W.; et al. Cross-scale monitoring and assessment of land degradation and sustainable land management: A methodological framework for knowledge management. Land Degrad. Dev. 2011 , 22 , 261–271. [ Google Scholar ] [ CrossRef ]
• Salih, A.; Ganawa, E.; Elmahl, A. Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery. Egypt. J. Remote Sens. Space Sci. 2017 , 20 , S21–S29. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Wang, J.; Deng, X. Rocky land desertification and its driving forces in the karst areas of rural Guangxi, Southwest China. J. Mt. Sci. 2008 , 5 , 350–357. [ Google Scholar ] [ CrossRef ]
• Winslow, M.; Akhtar-Schuster, M.; Martius, C.; Stringer, L.; Thomas, R.; Vogt, J. Special Issue on Understanding Dryland Degradation Trends. Land Degrad. Dev. 2011 , 22 , 145–312. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Warrena, A. Land degradation is contextual. Land Degrad. Dev. 2002 , 13 , 449–459. [ Google Scholar ] [ CrossRef ]
• Yue, Y.; Li, M.; Zhu, A.; Ye, X.; Mao, R.; Wan, J.; Dong, J. Land Degradation Monitoring in the Ordos Plateau of China Using an Expert Knowledge and BP-ANN-Based Approach. Sustainability 2016 , 8 , 1174. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kust, G.; Andreeva, O.; Lobkovskiy, V.; Telnova, N. Uncertainties and policy challenges in implementing Land Degradation Neutrality in Russia. Environ. Sci. Policy 2018 , 89 , 348–356. [ Google Scholar ] [ CrossRef ]
• Abdel-Kader, F. Assessment and monitoring of land degradation in the northwest coast region, Egypt using Earth observations data. Egypt. J. Remote Sens. Space Sci. 2018 . [ Google Scholar ] [ CrossRef ]
• Grinblat, Y.; Kidron, G.; Karnieli, A.; Benenson, I. Simulating land-use degradation in West Africa with the ALADYN model. J. Arid. Environ. 2015 , 112 , 52–63. [ Google Scholar ] [ CrossRef ]
• Keesstra, S.D.; Bouma, J.; Wallinga, J.; Tittonell, P.; Smith, P.; Cerdà, A.; Fresco, L.O. The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2016 , 2 , 111–128. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Keesstra, S.; Mol, G.; De Leeuw, J.; Okx, J.; Molenaar, C.; De Cleen, M.; Visser, S. Soil-Related Sustainable Development Goals: Four Concepts to Make Land Degradation Neutrality and Restoration Work. Land 2018 , 7 , 133. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hazbavi, Z.; Sadeghi, S.H.R.; Gholamalifard, M. Dynamic analysis of soil erosion-based watershed health. Geogr. Environ. Sustain. 2019 , 3 , 43–59. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rodrigo-Comino, J.; Senciales, J.M.; Cerdà, A.; Brevik, E.C. The multidisciplinary origin of soil geography: A review. Earth-Sci. Rev. 2018 , 177 , 114–123. [ Google Scholar ] [ CrossRef ]
• Rodrigo-Comino, J.; Barrena-González, J.; Pulido-Fernández, M.; Cerdá, A. Estimating Non-Sustainable Soil Erosion Rates in the Tierra de Barros Vineyards (Extremadura, Spain) Using an ISUM Update. Appl. Sci. 2019 , 9 , 3317. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bayat, F.; Monfared, A.B.; Jahansooz, M.R.; Esparza, E.T.; Keshavarzi, A.; Morera, A.G.; Cerdà, A. Analyzing long-term soil erosion in a ridge-shaped persimmon plantation in eastern Spain by means of ISUM measurements. Catena 2019 , 183 , 104176. [ Google Scholar ] [ CrossRef ]
• Lal, R. Tillage effects on soil degradation, soil resilience, soil quality, and sustainability. Soil Tillage Res. 1993 , 27 , 1–8. [ Google Scholar ] [ CrossRef ]
• Thomaz, E.; Luiz, J. Soil loss, soil degradation and rehabilitation in a degraded land area in Guarapuava (Brazil). Land Degrad. Dev. 2010 , 23 , 72–81. [ Google Scholar ] [ CrossRef ]
• Grum, B.; Assefa, D.; Hessel, R.; Woldearegay, K.; Kessler, A.; Ritsema, C.; Geissen, V. Effect of In Situ Water Harvesting Techniques on Soil and Nutrient Losses in Semi-Arid Northern Ethiopia. Land Degrad. Dev. 2016 , 28 , 1016–1027. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mamy, L.; Vrignaud, P.; Cheviron, N.; Perreau, F.; Belkacem, M.; Brault, A. No evidence for effect of soil compaction on the degradation and impact of isoproturon. Environ. Chem. Lett. 2010 , 9 , 145–150. [ Google Scholar ] [ CrossRef ]
• Khaledian, Y.; Kiani, F.; Ebrahimi, S.; Brevik, E.; Aitkenhead-Peterson, J. Assessment and Monitoring of Soil Degradation during Land Use Change Using Multivariate Analysis. Land Degrad. Dev. 2016 , 28 , 128–141. [ Google Scholar ] [ CrossRef ]
• Young, A.; Saunders, I. Rates of surface processes and denudation. In Hillslope Processes ; Abrahams, A.D., Ed.; United Kingdom: The Bin-ghamton Symposia in Geomorphology, International Series; John Wiley & Sons: Hoboken, NJ, USA, 1986; Volume 16, pp. 3–27. [ Google Scholar ]
• Koch, A.; McBratney, A.; Adams, M.; Field, D.; Hill, R.; Crawford, J. Soil Security: Solving the Global Soil Crisis. Glob. Policy 2013 , 4 , 434–441. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Weinzierl, T.; Wehberg, J.; Böhner, J.; Conrad, O. Spatial Assessment of Land Degradation Risk for the Okavango River Catchment, Southern Africa. Land Degrad. Dev. 2015 , 27 , 281–294. [ Google Scholar ] [ CrossRef ]
• Hazbavi, Z.; Davudirad, A.A.; Alaei, N. Overview on Land Degradation in The Industrial Shazand Watershed ; LAP LAMBERT Academic Publishing: Saarbrücken, Germany, 2019. [ Google Scholar ]
• Velmourougane, K.; Blaise, D. Soil Health, Crop Productivity and Sustainability Challenges. Sustain. Chall. Agrofood Sector 2017 , 21 , 509–531. [ Google Scholar ]
• Ajayi, A. Land degradation and the sustainability of agricultural production in Nigeria: A review. J. Soil Sci. Environ. Manag. 2015 , 6 , 234–240. [ Google Scholar ]
• Hoffman, M.; Todd, S. A National Review of Land Degradation in South Africa: The Influence of Biophysical and Socio-economic Factors. J. South. Afr. Stud. 2000 , 26 , 743–758. [ Google Scholar ] [ CrossRef ]
• Bornmann, L.; Marx, W. Critical rationalism and the search for standard (field-normalized) indicators in bibliometrics. J. Informetr. 2018 , 12 , 598–604. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Torres, L.; Abraham, E.; Rubio, C.; Barbero-Sierra, C.; Ruiz-Pérez, M. Desertification Research in Argentina. Land Degrad. Dev. 2015 , 26 , 433–440. [ Google Scholar ] [ CrossRef ]
• Escadafal, R.; Barbero-Sierra, C.; Exbrayat, W.; Marques, M.; Akhtar-Schuster, M.; El Haddadi, A.; Ruiz, M. First Appraisal of the Current Structure of Research on Land and Soil Degradation as Evidenced by Bibliometric Analysis of Publications on Desertification. Land Degrad. Dev. 2015 , 26 , 413–422. [ Google Scholar ] [ CrossRef ]
• Zupic, I.; Cater, T. Bibliometric methods in management and organization. Organ. Res. Methods. 2015 , 18 , 429–472. [ Google Scholar ] [ CrossRef ]
• Gagolewski, M. Bibliometric impact assessment with R and the CITAN package. J. Informetr. 2011 , 5 , 678–692. [ Google Scholar ] [ CrossRef ]
• Alavifard, S. Hindex Calculator: H-index Calculator Using Data from a Web of Science (WoS) Citation Report. R PACKAGE version 1.0.0. 2015. Available online: https://CRAN.R-project.org/package=hindexcalculator (accessed on 11 September 2015).
• Uddin, A. scientoText: Text & Scientometric Analytics. R Package Version 0.1. 2016. Available online: https://CRAN.R-project.org/package=scientoText (accessed on 20 July 2016).
• Aria, M.; Cuccurullo, C. Bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017 , 11 , 959–975. [ Google Scholar ] [ CrossRef ]
• Wessels, K.; Van den Bergh, F.; Scholes, R.J. Limits to detectability of land degradation by trend analysis of vegetation index data. Remote Sens. Environ. 2012 , 125 , 10–22. [ Google Scholar ] [ CrossRef ]
• Wessels, K.; Prince, S.; Reshef, I. Mapping land degradation by comparison of vegetation production to spatially derived estimates of potential production. J. Arid. Environ. 2008 , 72 , 1940–1949. [ Google Scholar ] [ CrossRef ]
• Evans, J.; Geerken, R. Discrimination between climate and human-induced dryland degradation. J. Arid. Environ. 2004 , 57 , 535–554. [ Google Scholar ] [ CrossRef ]
• Tong, C.; Wu, J.; Yong, S.; Yang, J.; Yong, W. A landscape-scale assessment of steppe degradation in the Xilin River Basin, Inner Mongolia, China. J. Arid. Environ. 2004 , 59 , 133–149. [ Google Scholar ] [ CrossRef ]
• Prince, S.; Becker-Reshef, I.; Rishmawi, K. Detection and mapping of long-term land degradation using local net production scaling: Application to Zimbabwe. Remote Sens. Environ. 2009 , 113 , 1046–1057. [ Google Scholar ] [ CrossRef ]
• Symeonakis, E.; Drake, N. Monitoring desertification and land degradation over sub-Saharan Africa. Int. J. Remote Sens. 2004 , 25 , 573–592. [ Google Scholar ] [ CrossRef ]
• Gisladottir, G.; Stocking, M. Land degradation control and its global environmental benefits. Land Degrad. Dev. 2005 , 16 , 99–112. [ Google Scholar ] [ CrossRef ]
• Vogt, J.V.; Safriel, U.; Von Maltitz, G.; Sokona, Y.; Zougmore, R.; Bastin, G.; Hill, J. Monitoring and assessment of land degradation and desertification: Towards new conceptual and integrated approaches. Land Degrad. Dev. 2011 , 22 , 150–165. [ Google Scholar ] [ CrossRef ]
• Contador, J.F.L.; Schnabel, S.; Gutiérrez, A.G.; Fernández, M.P. Mapping sensitivity to land degradation in Extremadura. SW Spain. Land Degrad. Dev. 2009 , 20 , 129–144. [ Google Scholar ] [ CrossRef ]
• Symeonakis, E.; Calvo-Cases, A.; Arnau-Rosalen, E. Land Use Change and Land Degradation in Southeastern Mediterranean Spain. Environ. Manag. 2007 , 40 , 80–94. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ravi, S.; Breshears, D.; Huxman, T.; D’Odorico, P. Land degradation in drylands: Interactions among hydrologic–aeolian erosion and vegetation dynamics. Geomorphology 2010 , 116 , 236–245. [ Google Scholar ] [ CrossRef ]
• Pickup, G.; Bastin, G.N.; Chewings, V.H. Identifying trends in land degradation in non-equilibrium rangelands. J. Appl. Ecol. 1998 , 35 , 365–377. [ Google Scholar ] [ CrossRef ]
• Chasek, P.; Safriel, U.; Shikongo, S.; Fuhrman, V. Operationalizing Zero Net Land Degradation: The next stage in international efforts to combat desertification? J. Arid. Environ. 2015 , 112 , 5–13. [ Google Scholar ] [ CrossRef ]
• Kelly, C.; Ferrara, A.; Wilson, G.; Ripullone, F.; Nolè, A.; Harmer, N.; Salvati, L. Community resilience and land degradation in forest and shrubland socio-ecological systems: Evidence from Gorgoglione, Basilicata, Italy. Land Use Pol. 2015 , 46 , 11–20. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Marchi, M.; Ferrara, C.; Biasi, R.; Salvia, R.; Salvati, L. Agro-Forest Management and Soil Degradation in Mediterranean Environments: Towards a Strategy for Sustainable Land Use in Vineyard and Olive Cropland. Sustainability 2018 , 10 , 2565. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Stringer, L.; Twyman, C.; Thomas, D. Combating Land Degradation through Participatory Means: The Case of Swaziland. AMBIO: A J. Hum. Environ. 2007 , 36 , 387–393. [ Google Scholar ] [ CrossRef ]
• Stringer, L.; Twyman, C.; Thomas, D. Learning to reduce degradation on Swaziland’s arable land: Enhancing understandings of Striga asiatica. Land Degrad. Dev. 2007 , 18 , 163–177. [ Google Scholar ] [ CrossRef ]
• Liu, M.; Dries, L.; Heijman, W.; Huang, J.; Zhu, X.; Hu, Y.; Chen, H. The Impact of Ecological Construction Programs on Grassland Conservation in Inner Mongolia, China. Land Degrad. Dev. 2017 , 29 , 326–336. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wang, L.; Zhang, J.; Liu, L. Diversification of rural livelihood strategies and its effect on local landscape restoration in the semiarid hilly area of the Loess Plateau, China. Land Degrad. Dev. 2010 , 21 , 433–445. [ Google Scholar ] [ CrossRef ]
• Lakshminarayan, P.; Gassman, P.; Bouzaher, A.; Izaurralde, R. A Metamodeling Approach to Evaluate Agricultural Policy Impact on Soil Degradation in Western Canada. Can. J. Agric. Econ-Rev. Can. Agroecon. 1996 , 44 , 277–294. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Estoque, R.C.; Xie, H.; Murayama, Y.; Ranagalage, M. Bibliometric analysis of highly cited articles on ecosystem services. PLoS ONE 2019 , 14 , e0210707. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Reynolds, J.; Grainger, A.; Stafford Smith, D.; Bastin, G.; Garcia-Barrios, L.; Fernández, R. Scientific concepts for an integrated analysis of desertification. Land Degrad. Dev. 2011 , 22 , 166–183. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Darkoh, M. The nature, causes and consequences of desertification in the drylands of Africa. Land Degrad. Dev. 1998 , 9 , 1–20. [ Google Scholar ] [ CrossRef ]
• Dharumarajan, S.; Bishop, T.; Hegde, R.; Singh, S. Desertification vulnerability index-an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India. Land Degrad. Dev. 2017 , 29 , 150–161. [ Google Scholar ] [ CrossRef ]
• Löw, F.; Navratil, P.; Kotte, K.; Schöler, H.; Bubenzer, O. Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea—A potential tool for assessing the hazard degree of dust and salt storms. Environ. Monit. Assess. 2013 , 185 , 8303–8319. [ Google Scholar ] [ CrossRef ]
• Sepehr, A.; Zucca, C. Ranking desertification indicators using TOPSIS algorithm. Nat. Hazards 2012 , 62 , 1137–1153. [ Google Scholar ] [ CrossRef ]
• Wang, C.; Wan, S.; Xing, X.; Zhang, L.; Han, X. Temperature and soil moisture interactively affected soil net N mineralization in temperate grassland in Northern China. Soil Biol. Biochem. 2006 , 38 , 1101–1110. [ Google Scholar ] [ CrossRef ]
• Zhao, X.; Zhu, H.; Dong, K.; Li, D. Plant Community and Succession in Lowland Grasslands under Saline–Alkali Conditions with Grazing Exclusion. Agron. J. 2017 , 109 , 2428. [ Google Scholar ] [ CrossRef ]
• Akiyama, T.; Kawamura, K. Grassland degradation in China: Methods of monitoring, management and restoration. Grassl. Sci. 2007 , 53 , 1–17. [ Google Scholar ] [ CrossRef ]
• Lal, R. Soil erosion impact on agronomic productivity and environment quality. Crit. Rev. Plant. Sci. 1998 , 17 , 319–464. [ Google Scholar ] [ CrossRef ]
• Moritani, S.; Yamamoto, T.; Andry, H.; Inoue, M.; Kaneuchi, T. Using digital photogrammetry to monitor soil erosion under conditions of simulated rainfall and wind. Aust. J. Soil Res. 2010 , 48 , 36–42. [ Google Scholar ] [ CrossRef ]
• Wu, X.; Wei, Y.; Wang, J.; Cai, C.; Deng, Y.; Xia, J. RUSLE erodibility of heavy-textured soils as affected by soil type, erosional degradation, and rainfall intensity: A field simulation. Land Degrad. Dev. 2018 , 29 , 408–421. [ Google Scholar ] [ CrossRef ]
• Ding, Y. Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. J. Informetr. 2011 , 5 , 187–203. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mori, Y.; Kuroda, M.; Makino, N. Multiple correspondence analysis. Encycl. Meas. Stat. 2014 , 29 , 91–116. [ Google Scholar ]
• Woo, M.; Young, K. Hydrogeomorphology of patchy wetlands in the high arctic, polar desert environment. Wetlands 2003 , 23 , 291–309. [ Google Scholar ] [ CrossRef ]
• Seilheimer, T.; Mahoney, T.; Chow-Fraser, P. Comparative study of ecological indices for assessing human-induced disturbance in coastal wetlands of the laurentian great lakes. Ecol. Indic. 2009 , 9 , 81–91. [ Google Scholar ] [ CrossRef ]
• Jacobs, A.; Kentula, M.; Herlihy, A. Developing an index of wetland condition from ecological data: An example using HGM functional variables from the Nanticoke watershed, USA. Ecol. Indic. 2010 , 10 , 703–712. [ Google Scholar ] [ CrossRef ]
• Richardson, C.; King, R.; Qian, S.; Vaithiyanathan, P.; Stow, C. Estimating ecological thresholds for phosphorus in the everglades. Environ. Sci. Technol. 2008 , 41 , 8084–8091. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lake, P.; Bond, N.; Reich, P. Linking ecological theory with stream restoration. Freshw. Biol. 2007 , 52 , 597–615. [ Google Scholar ] [ CrossRef ]
• Niedermeier, A.; Robinson, J. Hydrological controls on soil redox dynamics in a peat-based, restored wetland. Geoderma 2007 , 137 , 318–326. [ Google Scholar ] [ CrossRef ]
• Datta, S.; Sarkar, K. Threatened access, risk of eviction and forest degradation: Case study of sustainability problem in a remote rural region in india. Environ. Dev. Sustain. 2012 , 14 , 153–165. [ Google Scholar ] [ CrossRef ]
• Salvati, L.; Carlucci, M. Towards sustainability in agro-forest systems? grazing intensity, soil degradation and the socioeconomic profile of rural communities in italy. Ecol. Econ. 2015 , 112 , 1–13. [ Google Scholar ] [ CrossRef ]
• Thompson, I. Biodiversity, ecosystem thresholds, resilience and forest degradation. Unasylva 2011 , 62 , 25–30. [ Google Scholar ]
• Lambin, E. Monitoring forest degradation in tropical regions by remote sensing: Some methodological issues. Glob. Ecol. Biogeogr. 1999 , 8 , 191–198. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Sui, L.; Yang, X.; Wang, Z.; Ge, D. Economic Globalization Impacts on the Ecological Environment of Inland Developing Countries: A Case Study of Laos from the Perspective of the Land Use/Cover Change. Sustainability 2019 , 11 , 3940. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Nguyen, T. Drivers of forest change in Hoa Binh, Vietnam in the context of integration and globalization. Singap. J. Trop. Geogr. 2019 , 40 , 452–475. [ Google Scholar ] [ CrossRef ]
• Li, S.; Verburg, P.; Lv, S.; Wu, J.; Li, X. Spatial analysis of the driving factors of grassland degradation under conditions of climate change and intensive use in Inner Mongolia, China. Reg. Envir. Chang. 2011 , 12 , 461–474. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Zha, Y.; Gao, J.; Ni, S. Assessment of grassland degradation near Lake Qinghai, West China, using Landsat TM andin situreflectance spectra data. Int. J. Remote Sens. 2004 , 25 , 4177–4189. [ Google Scholar ] [ CrossRef ]
• Gao, Q.; Wan, Y.; Xu, H.; Li, Y.; Jiangcun, W.; Borjigidai, A. Alpine grassland degradation index and its response to recent climate variability in Northern Tibet, China. Quat. Int. 2010 , 226 , 143–150. [ Google Scholar ] [ CrossRef ]
• Maitima, J.; Mugatha, S.; Reid, R.; Gachimbi, L.; Majule, A.; Lyaruu, H. The linkages between land use change, land degradation and biodiversity across east africa. Afr. J. Environ. Sci. Technol. 2004 , 3 , 310–325. [ Google Scholar ]
• De Valença, A.; Vanek, S.; Meza, K.; Ccanto, R.; Olivera, E. Land use as a driver of soil fertility and biodiversity across an agricultural landscape in the Central Peruvian Andes. Ecol. Appl. 2017 , 27 , 1138–1154. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vityakon, P. Degradation and restoration of sandy soils under different agricultural land uses in northeast Thailand: A review. Land Degrad. Dev. 2007 , 18 , 567–577. [ Google Scholar ] [ CrossRef ]
• Ahirwal, J.; Maiti, S.; Singh, A. Ecological Restoration of Coal Mine-Degraded Lands in Dry Tropical Climate: What Has Been Done and What Needs to Be Done? Environ. Qual. Manag. 2016 , 26 , 25–36. [ Google Scholar ] [ CrossRef ]
• Martin, L.; Moloney, K.; Wilsey, B. An assessment of grassland restoration success using species diversity components. J. Appl. Ecol. 2005 , 42 , 327–336. [ Google Scholar ] [ CrossRef ]
• Liu, L.; Luo, X. Understanding Farmers’ Perceptions and Behaviors towards Farmland Quality Change in Northeast China: A Structural Equation Modeling Approach. Sustainability 2018 , 10 , 3345. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cowie, A.; Penman, T.; Gorissen, L.; Winslow, M.; Lehmann, J.; Tyrrell, T. Towards sustainable land management in the drylands: Scientific connections in monitoring and assessing dryland degradation, climate change and biodiversity. Land Degrad. Dev. 2011 , 22 , 248–260. [ Google Scholar ] [ CrossRef ]
• Thomas, R.; Reed, M.; Clifton, K.; Appadurai, N.; Mills, A.; Zucca, C. A framework for scaling sustainable land management options. Land Degrad. Dev. 2018 , 29 , 3272–3284. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Ghassemi, F.; Jakeman, A.; Nix, H. Salinisation of Land and Water Resources: Human Causes, Extent, Management and Case Studies ; CAB International: Wallingford, UK, 1995. [ Google Scholar ]
• Qadir, M.; Noble, A.; Schubert, S.; Thomas, R.; Arslan, A. Sodicity-induced land degradation and its sustainable management: Problems and prospects. Land Degrad. Dev. 2006 , 17 , 661–676. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Wang, Y. Rural land engineering and poverty alleviation: Lessons from typical regions in China. J. Geogr. Sci. 2019 , 29 , 643–657. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Cobo, M.; López-Herrera, A.; Herrera-Viedma, E.; Herrera, F. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J. Informetr. 2011 , 5 , 146–166. [ Google Scholar ] [ CrossRef ]
• Soundararajan, K.; Ho, H.; Su, B. Sankey diagram framework for energy and exergy flows. Appl. Energy 2014 , 136 , 1035–1042. [ Google Scholar ] [ CrossRef ]
• Baroudy, A. Monitoring land degradation using remote sensing and GIS techniques in an area of the middle Nile Delta, Egypt. Catena 2011 , 87 , 201–208. [ Google Scholar ] [ CrossRef ]
• Eckert, S.; Hüsler, F.; Liniger, H.; Hodel, E. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. J. Arid. Environ. 2015 , 113 , 16–28. [ Google Scholar ] [ CrossRef ]
• Schulte, R.; Creamer, R.; Donnellan, T.; Farrelly, N.; Fealy, R.; O’Donoghue, C.; O’hUallachain, D. Functional land management: A framework for managing soil-based ecosystem services for the sustainable intensification of agriculture. Environ. Sci. Policy 2014 , 38 , 45–58. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rey Benayas, J.; Bullock, J. Restoration of Biodiversity and Ecosystem Services on Agricultural Land. Ecosystems 2012 , 15 , 883–899. [ Google Scholar ] [ CrossRef ]
• Requier-Desjardins, M.; Adhikari, B.; Sperlich, S. Some notes on the economic assessment of land degradation. Land Degrad. Dev. 2010 , 22 , 285–298. [ Google Scholar ] [ CrossRef ]
• Zhang, K.; Yu, Z.; Li, X.; Zhou, W.; Zhang, D. Land use change and land degradation in China from 1991 to 2001. Land Degrad. Dev. 2007 , 18 , 209–219. [ Google Scholar ] [ CrossRef ]
• Batunacun; Nendel, C.; Hu, Y.; Lakes, T. Land-use change and land degradation on the Mongolian Plateau from 1975 to 2015-A case study from Xilingol, China. Land Degrad. Dev. 2018 , 29 , 1595–1606. [ Google Scholar ] [ CrossRef ]
• Wu, J.; Gong, Y.; Zhou, J.; Wang, X.; Gao, J.; Yan, A. The governance of integrated ecosystem management in ecological function conservation areas in China. Reg. Envir. Chang. 2013 , 13 , 1301–1312. [ Google Scholar ] [ CrossRef ]
• Wu, J. Landscape sustainability science: Ecosystem services and human well-being in changing landscapes. Landsc. Ecol. 2013 , 28 , 999–1023. [ Google Scholar ] [ CrossRef ]
• Liniger, H.; Harari, N.; Van Lynden, G.; Fleiner, R.; De Leeuw, J.; Bai, Z.; Critchley, W. Achieving land degradation neutrality: The role of SLM knowledge in evidence-based decision-making. Environ. Sci. Policy 2019 , 94 , 123–134. [ Google Scholar ] [ CrossRef ]

Share and Cite

Xie, H.; Zhang, Y.; Wu, Z.; Lv, T. A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land 2020 , 9 , 28. https://doi.org/10.3390/land9010028

Xie H, Zhang Y, Wu Z, Lv T. A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions. Land . 2020; 9(1):28. https://doi.org/10.3390/land9010028

Xie, Hualin, Yanwei Zhang, Zhilong Wu, and Tiangui Lv. 2020. "A Bibliometric Analysis on Land Degradation: Current Status, Development, and Future Directions" Land 9, no. 1: 28. https://doi.org/10.3390/land9010028

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• Open access
• Published: 25 April 2019

Assessment of land degradation and implications on agricultural land in Qalyubia Governorate, Egypt

• W. A. M. Abdel Kawy 1 &
• Kh. M. Darwish   ORCID: orcid.org/0000-0002-8197-3511 2  

Bulletin of the National Research Centre volume  43 , Article number:  70 ( 2019 ) Cite this article

5794 Accesses

6 Citations

Metrics details

Land degradation considers as a phenomena or more that decrease the current and/or the potential soil capability to produce goods. It signifies a regression from a higher to lower state, owing to descend in land capability, productivity, and decline of biodiversity. This study is an attempt to address the complexity of land degradation issue, particularly in the targeted farming irrigated fields, Qalyubia Governorate, Egypt. It aims to assess and observe degradation hazard through satellite image analyze, model trends of degradation, and generate a change detection hazard map for the investigated area.

The maximum likelihood supervised classification tool and post classification change detection technique were implemented for monitoring changes in land qualities. Land degradation indicator data layers were summarized into the metrological data, ground truth, vegetation cover, and the applied land management practices. The Modified Global Assessment of Soil Degradation GLASOD model was adapted to model land degradation, specify its action in term of degradation degree, relative extent, severity level, and causative factors. Land degradation processes were evaluated in the delineated mapping units. The result indicated that the studied areas are considered as quite unstable in term of ecosystem due to active degradation resulting from aridity phenomena, soil properties, and improper farm management.

The most active land degradation processes are assessment of salinization, waterlogging, alkalization, and soil compaction.

Introduction

The United Nations Convention to Combat Desertification (UNCCD) recognizes land degradation as one of the most noticeable environmental concerns of recent times (UNCCD 1994 , 2002 ). According to Baylis et al. 2012 and UNCCD 2013 , they reported the sequences as a result of land degradation processes; nearly 40–75% of the world’s agricultural land’s productivity is reduced.

Land degradation is often described as substantial decrease in the biological productivity of land system, due to natural events exaggerated by anthropogenic activities (Johnson and Lewis 2007 ). Land degradation in dry land is often termed as desertification (Dregn 2002 ). Most forms of land degradation are man-made problems. Although there are some physical environmental factors involved, but misuse is an important factor. Poor land management with the intensification of agriculture practices accelerates the rate of land degradation (Wim and El-Hadji 2002 ). Food supply situation will be worse in the future, if the current tendency of land degradation did not change drastically. In Egypt, land resources degradation is the main limitation to the agricultural sector development, where the ratio between land resources and population rate is the most critical problem currently. In arid and semi-arid conditions, the salinization and/or alkalization as subsequent of water logging are the major land degradation processes in irrigated agriculture fields (Ayoub 1991 ; Dwivedi et al. 1999 ; El-Kassas 1999 ).

Normally, inter-relationship between land degradation and socioeconomic activities is a multi-layered and complex issue. Several multi-disciplinary approaches integrated with multiple data sources and methodologies to study the implications of land degradation are need of the hour. Monitoring and assessment of regional land degradation or restoration of land is very important so as to understand the dynamic trend of land degradation or restoration, providing better scientific prevention and environmental control (Sun et al. 2017 ). To address the gaining issue and a systematic understanding of the changes magnitude in land degradation at the temporal and regional scales, remote sensing and geographical information system (GIS) have been selected as the best utilities.

In the area under investigation, the current research aims to highlight various land degradation processes using a GIS platform and remote sensing (RS) data. Asses human-induced land degradation and evaluate loss of soil productivity, which is considered as a degradation factor meaningful to desertification caused by land mismanagement and human influence. Detection of land use/cover change technique that is used to monitor agricultural land among dates 1967–2017. The evaluation of soil capability lost.

Regionally, Qalubiya is one of the largest governorates, which is located in the eastern side of the River Nile. Officially, Qalubiya includes eight districts, and its capital town is called Banha. Mostly, the major activity is cultivation, where there is some existence of industrial zones. The area under investigation incorporates a surface area of approximately 224,363 ac (90.8 ha). It is bounded between 30° 06′ 11″ and 30° 36′ 36″ North and 31° 03′ 20″ and 31° 35′ 32″ East Fig.  1 . According to the Egyptian Meteorological Authority ( 2016 ), the study area falls in the arid zone, where the soil temperature regime could be defined as thermic and moisture regime is torric (Soil survey staff 2014 ).

Location of the study area

Geologically, the area belongs to the late Pleistocene that represented by deposits of the neonile broke into Egypt, often in the earlier part of this age and by the deposits too that was accumulated during the recessional phases of the river. Through its history, the neonile in this massif has been continuously lowering its course at a rate of 1 m/1000 years (Said 1993 ).

Material and methods

Landform mapping.

Collection of data sources includes soil map of Egypt (ASRT 1982 ), topographic-sheets (scale 1:25,000), and a Landsat-8 satellite image was used to cover the study area, which was acquired in August, 2016, with a path (176) and row (39) from the (USGS) Geologic Survey archive ( http://earthexplorer.usgs.gov/ ) (Fig.  2 ). As an image preprocessing analysis, the image was calibrated to radiance using the inputs of image type, acquisition date, and time, then it was stretched using linear 2%, smoothly filtered, and their histograms were matched (Lillesand and Kiefer 2007 ). Atmospheric correction was done, then images were mosaicked and geometrically rectified using ENVI 5.1 software (ITT 2009 ). High spatial resolution satellite data are needed for delineation of landform features, so the spatial resolution of Landsat-8 image was enhanced through merging process of the higher spatial resolution panchromatic data (band 8). This process is applied and resulting in multi-spectral data with high spatial resolution (14.25 m). The geomorphology layer was generated by the integration of contour lines extracted from the digital elevation data DEM that derived from topo sheets integrated with SRTM 1 arc second data and the enhanced Landsat-8 image using ENVI 5.1 software (Dobos et al. 2002 ). Physiographic map of the study area has been produced using physiographic analysis, then map legend was established according to Zink and Valenzuala ( 1990 ).

Landsat-8 image of the investigated area (FCC bands 7,5,3)

A total of 15 soil profiles were taken to represent different mapping units. The ground truth data, field survey, soil profile morphological and pedological investigations, and analytical data reveal the main characteristics of different land form map units.

Laboratory analysis

The field-collected representative soil samples were analyzed according to US Soil Survey Staff ( 2014 ). The collected soil samples and auger observations were first air dried, then ground gently, and sieved through 2-mm sieve, where the main physical and chemical properties were determined based on the laboratory routine analysis procedures (Richard 1954 ; Page et al. 1982 ). Soil classification and taxonomy was done using Soil survey staff ( 2014 ). Worth to mention that the soil correlation between the physiographic and taxonomic units were designed in order to identify the major soil sets of the studied area, after Elberson and Catalan ( 1987 ). ArcGIS 10.3 was the main GIS platform used in this study.

Land degradation status

The conceptual framework applied to the work is based on a comparison study between the data extracted from the soil survey of El-Qalyubia governorate report RISW ( 1967 ), and the more recent ground truth data been done in 2017. In regard to the pedoloical, topography features, and climatic factors that are defined and described according to FAO/UNEP ( 1978 , 1979 ) methodology for assessing soil degradation, the natural vulnerability for each soil profile was evaluated and confirmed with the physiographic units. The rating used is presented in Tables  1 and 2 ) and the soil degradation classed and rates are shown in Table  3 . The status of soil degradation is an expression of the process severity. The severity of the processes is characterized by the degree in which the soil degraded and by the relative extent of the degraded area with in a delineated physiographic unit.

The degradation degree, relative extent, severity level, and causative factors were defined and modeled by using the Global Assessment of Soil Degradation (GLASOD) approach (UNEP 1991 ) as follows:

Degree of soil degradation: the criteria used to determine the degree of degradation is illustrated in Table  4 .

Relative extent of the degradation type: due to the mapping complication of separating areas of soil degradation individually, it was possible to estimate the relative extent of each type of soil degradation within the map unit. The following five categories are recognized:

▪ Infrequent: up to 5% of the unit is affected.

▪ 2-Common: 6–10% of the unit is affected.

▪ 3-Frequent: 11–25% of the unit is affected.

▪ 4-Very frequent: 26–50% of the unit is affected.

▪ 5-Dominate over: 50% of the unit is affected.

The severity level of soil degradation: the severity level is indicated by combination of the degree and relative extent as shown in Table  5 .

Causative factors: causative factors of the different land degradation types were identified in the field and also collected from the available technical reports.

Physiographic and soil map

Physiographic map of the investigated area has been generated using physiographic analysis (Zinck and Valenzuala 1990 ) by combining Landsat 8 satellite image and Digital Elevation Model DEM derived from topographic maps in integration with the Shuttle Radar Topography Mission SRTM data. According to the soil profile morphological description, sample analysis, and classification, soils were classified into two main orders (Aridisols and Entisols) and ten great groups were identified as shown in Fig.  3 and Table  6 . The obtained physiography map revealed that the island is occupied 0.59% of the investigated area, while the sub-island 1.12% and the levee 1.44%. The over flow mantle is occupied 14.26, the over flow basin 27.17%, the decantation basin 48.34%, the turtle backs 0.27%, and the sequence of river terraces form 6.81%.

Physiography and soil map of Qalyubia Governorate

According to the soil taxonomy classification (Soil Survey Staff 2014 ), studied soils could be classified as:

▪ I, Typic Torripsamment (cons.)—SI, Typic Torripsamment (cons.)

▪ L, Typic Torripsamment (cons.)—O.M, Typic Torrifluvent (cons.)

▪ O.M, Typic Paleargids (Assoc.)—O.B, Vertic Torrifluvent (cons.)

▪ O.B, Typic Natrargids (Assoc.)—D.B, Typic Torrifluvent (Cons.)

▪ T.B, Typic Torripsamment (Assoc.)—T, Vertic Torrifluvent (Cons.)

The physiographic and soil map legend of the investigated area is shown in Table  6 .

Land degradation assessment

Natural vulnerability.

Vulnerability means the potential to be harmed. Natural vulnerability encompasses the conditions determined by physical, social-economic, and environmental processes that increase the susceptibility of a land to the impact of natural hazards (UNISDR 2009 ). In principle, the agricultural land in Egypt is characterized by being among the most intensive agricultural use systems; it may reach three crop rotations a year according to the Egyptian crop calendar followed. These have contributed to the excessive cultivation of land, with the consequence of poor crop production. This misuse practice has not only negative effect on areas of the fertile land but also decrease the overall agro-exports from vegetables and fruits and hence add a lot of burden on the Egyptian economy.

The natural vulnerability and its relative extent percentage of different mapping units in the area under investigation are illustrated in Table  7 and Fig.  4 . The obtained data elucidated that soil of the map units (I, SI, L, O.M, T.B, and T) have a physical degradation ranging from moderate to high risk. In turn, the physical high risk degradation type is related to high content of silt fraction and low percentage of soil organic matter. Nevertheless, soils of (O.B and D.B) have a slight physical degradation related to low content of silt and high percentage of organic matter. In addition, the map units (I, SI, L, O.M, T.B, and T) exposed a chemical degradation risk ranging from slight to moderate. Moreover, units (O.B and D.B) exhibit a high chemical degradation risk due to high evapotranspiration value compared with the actual received amount of precipitation and irrigation water. The relative extent percentage (%) of the natural vulnerability classes are presented in Table  8 .

Natural vulnerability map

Human-induced land degradation

Definitely, human-induced land degradation is an actual increasing problem. Growth of population, agricultural pressure, unsustainable management of natural resources, as well as increasing amounts of harmful chemicals added to the environment all lead to severe land degradation. This phenomenon has to be considered from different prospective, e.g., agrarian, economic, cultural, and social conditions. The GLASOD approach is a first attempt to generate real maps on the status of human-induced land degradation (UNEP 1991 ).

The human-induced land degradation in the studied areas were assessed throughout the identification of rate, degree, relative extent, causative factors, and severity level of each type of land degradation (water logging, compaction, salinization, and alkalization) for the different mapping units as follow.

Land degradation rate

The rate of land degradation was estimated though a tabular comparison of the main land characteristics in (1967) and (2017) (Table  9 ). The degradation rate for each mapping unit was classified to slight as shown in Table  10 . The data revealed that the rate of salinization, alkalization, and compaction are slight. The annual increase of the soil electrical conductivity (ECe), exchangeable sodium percentage (ESP), and bulk density were reached to (0.1 dS/m), (0.2%), and (0.01 g/cm 3 ) respectively. In the study area, the rate of water logging is slight to moderate as the maximum increase of water table is (1.7 cm/year).

Degree of land degradation

The measured values of ECe, ESP, bulk density and water table depth are presented in range between (4.00–11.23 dS/m), (9.6–19.8%), (1.123–1.46 g/cm3), and (65–125 cm) in the order already mentioned. The hazards of land degradation types differ from low to moderate.

Relative extent of land degradation

The relative extent percentage of each type of human induced land degradation was estimated based upon the correlation between land physiography and soils in the various mapping units as illustrated in Table  11 .

Interpretation and identification of degraded areas by analysis of satellite image data and modeling are considered initial steps to address land degradation issues in Qalyubia Governorate. The GLASOD model adapted for this research is competent in terms of its flexibility; it allows for modification to accommodate indicators of land degradation.

Causative factors of human-induced land degradation

In the area under investigation, the main causative factors of human-induced land degradation types are mentioned in Table  12 and Fig.  5 . Taken into account the environmental factors and processes that would occur without human interference, the soil degradation is resulted when soils are not properly managed, misused, or inefficient utilized. The main types of human-induced land degradation are salinization, alkalization, soil compaction, and water logging; these types are affected as follow:

Salinization and alkalization: the human-induced salinization and alkalization could result from three causes. First, it can be the result of poor management of irrigation schemes. A high salt content of the irrigation water or too little attention given to the drainage status of irrigated field that can easily lead to rapid salinization and/or alkalization problems. This type of salt accumulation mainly occurs under arid and semi-arid condition. Second, salinization and/or alkalization will occur if sea water or fossil saline ground water bodies intrude onto agricultural lands and intrude the groundwater reserves and reservoirs of good quality. This sometimes happens in the coastal regions with an excessive use of groundwater, but can also occur in closed basin with aquifers of different salt content. Worth to mention that a third type happens where human activities lead to an increase in evapo-transpiration of soil moisture in land of high salt-containing parent materials or with saline ground water. In the study area of Qalyubia governorate, the causes of soil salinization could be due to the interactions of various factors: limited available supply of irrigation water, shallow groundwater table, water salinity, poor drainage conditions, parent material, topography, poor management, and climatic factors (high temperature, high evaporation rate, and humidity action) (Fig.  5 ). These factors were captured in questionnaires administered during the field study.

Compaction: this mainly occurs in the soils with a low physical structural stability, under the improper human activities. In the studied areas, soil compaction resulted from inexpedient management and improperly timed use of heavy machinery, misapply of irrigation, absence of conservative measurements, shortage of the fallow period, and an excessive application of harmful chemical fertilizers. This is a major observed degradation characteristic in the study area, which in return would decrease the yield and compact the soil, making it difficult to till the land. As the soil becomes compacted, aeration becomes limited; hence, such lands become less suitable for farming.

Water logging: human intervention in the natural drainage systems by the misuse of irrigation water quality may lead to flooding especially in heavy clay soils. Over irrigation, inefficient drainage system, and destruction of subsurface drainage networks (in some parts) are the main causes of water logging in the considered areas.

Land degradation causative factors over the study area

Severity level

The severity level of land degradation is indicated by a combination of the degradation degree and relative extent of degradation types (Table  13 ).

Status of land degradation

The obtained data of degradation rate, degree, extent, causative factors, and the severity levels in the different mapping units of the studied area are shown in Table  14 and Fig.  6 .

Status of land degradation over Qalyubia governorate

Agricultural land experience rapid changes due to natural and manmade factors. Monitoring these changes is essential for sustainable planning, resource management, and updating geospatial information systems. In general, the agricultural soils in Qalyubia governorate are characterized by quite good soil productivity. Soil units of the study area have a low degradation rate for different types of human-induced factors due to the less change in land characteristics in the last 50 years. This is obtained from monitoring changes in land characteristics during the time period of 1967 and 2017. The integration of remote sensing data and GIS utilities would provide a more precise information on observing the nature and spatial distribution of land use/cover changes and in elaborating the degradation degree. In regard to the present values of soil depth, bulk density, electric conductivity ECe, and exchangeable sodium percentage ESP, the soil units are threatening by a low to moderate degree of water logging, compaction, salinity, and alkalinity as a result of active degradation processes. The moderate values of these types are due to the over irrigation system applied, poor management of irrigation scheme, improper use of heavy machinery, absence of conservation measurements, excessive farm application of harmful chemical fertilizers, and other cultural factors. The severity level of the different degradation types in the targeted soils are indicated as low to very high level. Mitigation of soil sealing by use of diverse media would assist in increase people’s awareness of the seriousness of land degradation and implications on agricultural land.

ASRT (1982) Soil map of Egypt, final report. Academy of Scientific Research and Technology (ASRT), Cairo

Google Scholar  

Ayoub AT (1991) An assessment of human induced soil degradation in Africa. U.N. environmental program, Nairobi, Kenya, Second Soil Sci. conf, Cairo

Baylis K, Jolejole M, Lipper L (2012) Land degradation’s implications on agricultural value of production in Ethiopia: a look inside the bowl. Presentation Papers at the International Association of Agricultural Economists (IAAE) Triennial Conference, Foz do Iguacu

Dobos E, Norman B, Bruee W, Luca M, Chris J, Erika M (2002) The use of DEM and satellite images for regional scale soil database. Proceedings of the 17th World Congress of Soil Science, Bangkok

Dregn HE (2002) Land degradation in the drylands. Arid Land Res Manag 16(2):99–132

Article   Google Scholar  

Dwivedi RS, Sreenivas K, Ramana KV (1999) Inventory of salt affected soils and water-logged areas: a remote sensing approach. Int J Remote Sens 20(8):1589–1599

Egyptian Meteorological Authority. Climatic Atlas of Egypt. Published report, Ministry of Transport, Arab Republic of Egypt: 2016

Elberson GWW, Catalan R (1987) Portable computer in physiographic soil survey. Proc. Intemat soil Sci., Cong, Homburg

El-Kassas M (1999) Desertification and land degradation in arid regions. Alla, El-Morfa, Kuwait (Arabic), p 258

FAO/UNEP. Methodology for assessing soil degradation. 2527, Rome, Italy; 1978

FAO/UNEP (1979) A Provisional methodology for degradation assessment. FAO, Rome, p 48

ITT. ITT corporation ENVI 5.1 software. 1133 Westchester Avenue, White Plains, NY 10604, USA; 2009

Johnson DL, Lewis LA (2007) Land degradation: creation and destruction. Rowman and Littlefield, Lanham, DM, Boulder, New York, Toronto, Oxford.

Lillesand TM, Kiefer RW (2007) Remote sensing and image interpretation, 5th edn. Wiley, New York, p 820 Academic

Page AL, Miller RH, Keeney DR (1982) Methods of soil analysis (part 2)—chemical and microbiological properties, 2nd edn. Amer. Soc. of Agron, Madison

Richard LA. Diagnosis and improvement of saline and alkali soils. U.S. Dept. of Agric. HandBook, 1954, No. 60

RISW (1967) Soil survey of El-Kaluibiea Governorate. Report No. 155, Research Institute of Soils and Water (RISW), Cairo

Said R (1993) The river Nile geology and hydrology and utilization. Britain, Pergamon Press, Oxford, p 320

Soil Survey Staff (2014) Keys to soil taxonomy, 12th edn. USDA-Natural Resources Conservation Service, Washington, DC

Sun B, Li Z, Gao Z, Guo Z, Wang B, Hu X, Bai L (2017) Grassland degradation and restoration monitoring and driving forces analysis based on long time-series remote sensing data in XilinGol league. Acta Ecol Sin 37(4):219–228

UNCCD (1994) United Nations convention to combat desertification in those countries experiencing serious drought and/or desertification, particularly in Africa. UN, Paris

UNCCD, (2002) Global Alarm: Dust and Sandstorms from the World’s Drylands, Asia Regional Coordinating Unit, Secretariat of the United Nations Convention to Combat Desertification (UNCCD–CRIC1), Bangkok.

UNCCD (2013) Economic and Social impacts of desertification, land degradation and drought. White Paper I. UNCCD 2nd Scientific Conference of United Nations Convention to Combat Desertification, prepared with the contributions of an international group of scientists. Available from: http://2sc.unccd.int (accessed 26 March 2013.), ISBN 978-92-95043-66-4, Bonn, Germany.

UNEP Staff. Global assessment of soil degradation, UNEP UN GLASOG Project: 1991

United Nations International Strategy for Disaster Reduction (UN/ISDR) (2009) UNISDR terminology on disaster risk reduction. UN/ISDR, Geneva

US Soil Survey Staff. Soil survey field and laboratory methods manual. Soil survey investigations report no. 51, version 2.0.; 2014. R. Burt and Soil survey staff (ed.). U.S. Department of Agriculture, Natural Resources Conservation Service

Wim G, El-Hadji M (2002) Causes, general extent and physical consequence of land degradation in arid, semi-arid and dry sub humid areas. Forest conservation and natural resources, forest dept. FAO, Rome

Zink JA, Valenzuala (1990) Soil geographic database: structure and application examples. ITC J. vol. 3. ITC, Enschede

Download references

Acknowledgements

Not applicable.

The funding resources mainly come through the contribution of both soil and water departments in the Faculty of Agric., Cairo Uni., in cooperation with the Land and Water Technologies Dept., Arid Lands Cultivation Research Institute, City of Scientific Research and Technological Applications, Borg Al-Arab city.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All figures, maps, and tables generated during this study are included in this published article.

Author information

Authors and affiliations.

Soils Science Department, Faculty of Agriculture, Cairo University, Giza, Egypt

W. A. M. Abdel Kawy

Land and Water Technologies Department, Arid Lands Cultivation Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Borg Al-Arab, Alexandria, Egypt

Kh. M. Darwish

You can also search for this author in PubMed   Google Scholar

Contributions

Both authors contribute to the conception, design of the work; the acquisition, analysis, interpretation of data; the creation of new software used in the work; and have drafted the work or substantively revised it. Both authors have approved the submitted version (and any substantially modified version that involves the author’s contribution to the study). Both authors have agreed to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Kh. M. Darwish .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Kawy, W.A.M.A., Darwish, K.M. Assessment of land degradation and implications on agricultural land in Qalyubia Governorate, Egypt. Bull Natl Res Cent 43 , 70 (2019). https://doi.org/10.1186/s42269-019-0102-1

Download citation

Received : 08 January 2019

Accepted : 25 March 2019

Published : 25 April 2019

DOI : https://doi.org/10.1186/s42269-019-0102-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Land degradation
• Degradation hazard
• Land qualities
• Qalyubia governorate

Risk Analysis of Soil Erosion Using Remote Sensing, GIS, and Machine Learning Models in Imbabura Province, Ecuador

• Original Research
• Published: 27 August 2024
• Volume 5 , article number  824 , ( 2024 )

Cite this article

• Fernando Garrido   ORCID: orcid.org/0000-0001-7779-9787 1 &
• Pedro Granda   ORCID: orcid.org/0000-0001-5638-5673 1  

Soil erosion occurs when natural or man-made agents tear away the top layer of the soil, making it extremely difficult to grow vegetation on the site. Wind and water (the two main causes of erosion) easily wash away soil if it is bare. Agriculture is probably the most crucial activity that accelerates soil erosion due to the amount of cultivated land and the number of agricultural practices that disturb the soil. Soil erosion poses a significant environmental challenge, adversely affecting soil fertility through nutrient loss and contributing to sedimentation in aquatic environments. Besides this, water-induced soil erosion stands out as the most severe form of land degradation observed in diverse regions, both locally and globally. This study analyzed the risk of soil erosion in the province of Imbabura, north part of Ecuador, using Machine Learning (ML) models, applying the Revised Universal Soil Loss Equation (RUSLE) Model involves utilizing remote sensing techniques with sufficient resolution spatial–temporal and spectral, thus identifying vulnerable areas according to factors that play an essential role in erosion processes, namely topography (LS), land use or cover management (C), rainfall erosivity (R), erodibility (K) and anti-erosive agricultural practices (P). The map indicating erosion probability zones was generated using Geographic Information Systems (GIS) tools, that most of the study area is located within the zone of low probability of erosion (64.73%), and a small section is located within the zone of high likelihood of erosion (4.17%). These discoveries can provide valuable insights into reducing and preventing soil erosion in Imbabura. So, these findings provide robust evidence for the efficiency of employing machine learning in soil erosion prediction. LightGBM, which utilizes decision tree algorithms, achieved a Classification Accuracy Rate (CAR) of over 75.7% and an area under the receiver operating characteristic (AUC) of more than 0.89, underscoring its effectiveness.

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

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Explore related subjects

• Artificial Intelligence

Data Availability

Data will be made available on request.

Olika G, Fikadu G, Gedefa B. GIS based soil loss assessment using RUSLE model: a case of Horo district, western Ethiopia. Heliyon. 9 (2023).

Osman KT. Soil erosion by water. In: Soil degradation, conservation and remediation. Dordrecht: Springer; 2014. https://doi.org/10.1007/978-94-007-7590-9_3 .

Chapter   Google Scholar  

Serbaji MM et al. Soil water erosion modeling in tunisia using rusle and gis integrated approaches and geospatial data. Land (2023).

Tamiru B, et al. mapping soil parameters with environmental covariates and land cover projection in tropical rainforest, hangadi watershed, Ethiopia”. Sustainability. 2023. https://doi.org/10.3390/su15021066 .

Article   Google Scholar  

Karydas CG, Panagos P, Gitas IZ. A classification of water erosion models according to their geospatial characteristics. Int J Digit Earth. 2014;7(3):229–50. https://doi.org/10.1080/17538947.2012.671380 .

Sidi Almouctar MA, Wu Y, Zhao F, Dossou JF. Soil erosion assessment using the RUSLE model and geospatial techniques (remote sensing and GIS) in South-Central Niger (Maradi Region). Water. 2021;13:3511. https://doi.org/10.3390/w13243511 .

Maina CW, Sang JK, Raude JM, Mutua BM, Moriasi DN. Sediment distribution, and accumulation in lake Naivasha, Kenya over the past 50 years. Lakes Amp Reserv. 2019;24:162–72.

Yang A, Wang C, Pang G, Long Y, Wang L, Cruse RM, Yang Q. Gully erosion susceptibility mapping in highly complex terrain using machine learning models. ISPRS Int J Geo-Inf. 2021;10:680. https://doi.org/10.3390/ijgi10100680 .

Moges K, Alemu B, Nega K, Terefe T. The impact of land use and land cover (LULC) dynamics on soil erosion and sediment yield in Ethiopia. Heliyon. 2019;5(12): e02981. https://doi.org/10.1016/j.heliyon.2019.e02981 .

Sarkar T, Mishra M. Soil erosion susceptibility mapping with the application of logistic regression and artificial neural network. J Geovisualization Spat Anal. 2018;2:1–17.

Google Scholar  

Sentinel Playground, https://apps.sentinel-hub.com/sentinel-playground , EO Browser, https://apps.sentinel-hub.com/eo-browser/ , Sentinel Hub, https://www.sentinel-hub.com , Sentinel Hub, Sinergise Ltd.

GEE. Google Earth Engine (2023) Last accessed: 9 Nov .2023. URL. https://earthengine.google.com/

QGIS.org, %Y. QGIS Geographic Information System. QGIS Association. http://www.qgis.org .

Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci Model Dev. 2015;8:1991–2007. https://doi.org/10.5194/gmd-8-1991-2015 .

Poggio L, de Sousa LM, Batjes NH, Heuvelink GBM, Kempen B, Ribeiro E, and Rossiter D. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021 , 2021.

FAO. Digital Soil Map of the World (DSMW)|Land &Water| Food and Agriculture Organization of the United Nations Land &Water |Food and Agriculture Organization of the United Nations. Available online: http://www.fao.org/land-water/land/landgovernance/land-resources-planning-toolbox/category/details/en/c/1026564/ (Accessed 10 June 2022).

Earthdata Search. 2019. Greenbelt, MD: Earth Science Data and Information System (ESDIS) Project, Earth Science Projects Division (ESPD), Flight Projects Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA). URL: https://search.earthdata.nasa.gov/

NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team. <i>ASTER DEM Product</i>. 2001, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/ASTER/AST14DEM.003 and Accessed 21 Sept 2023.

European Space Agency, Sinergise (2021). Copernicus Global Digital Elevation Model. Distributed by OpenTopography. https://doi.org/10.5069/G9028PQB . Accessed: 21 Mar 2023

McMahon C. Lidar Survey of the San Pedro River, AZ 2021. National Center for Airborne Laser Mapping (NCALM). Distributed by OpenTopography. 2022. https://doi.org/10.5069/G98050T2 . Accessed: 18 Nov 2022.

Hook S, Fisher J. ECOSTRESS Evapotranspiration PT-JPL Daily L3 Global 70 m V001. 2019, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center, https://doi.org/10.5067/ECOSTRESS/ECO3ETPTJPL.001 and Accessed 21 Sept 2023.

Aleksandrov M, Batic M, Kadunc M, Kenda K, Milcinski G, Mocnik R, Peressutti D, Sovdat B, Zupanc A. Democratizing Earth Observation Big Data With eo-learn: Application to Water-Level Monitoring. 2018

eo-learn. Eo-learn Python packet. 2023. Last accessed 11 Nov 2023, URL https://eo-learn.readthedocs.io/en/latest/index.html .

ESRI. ArcGIS Online. 2023. URL. https://www.arcgis.com/ . Last accessed: 20 Nov 2023, URL Personal account. https://jfgs.maps.arcgis.com/

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3146–54.

Wischmeier WH, Smith DD. Predicting rainfall erosion losses - a guide to conservation planning. Agriculture handbook No. 537: US department of agriculture, Washington DC. 1978.

Nasir MJ, Alam S, Ahmad W, et al. Geospatial soil loss risk assessment using RUSLE model: a study of Panjkora River Basin, Khyber Pakhtunkhwa, Pakistan. Arab J Geosci. 2023;16:440. https://doi.org/10.1007/s12517-023-11555-2 .

Renard KG et al. “Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE).” 1997.

Lu H, Gallant J, Prosser IP, Moran CJ, Priestley, G. Prediction of sheet and rill erosion over the Australian continent, incorporating monthly soil loss distribution. CSIRO Land and Water; 2001-05. procite:035bf07f-eb74-4dc7-99f2-b1acab1a679e.

Geler T, Penteado A, Perez A. LS-Factor Analysis in different prediction models of water soil erosion. Instituto de Geografía Tropical, CITMA, Cuba, Instituto de Geociências, UNICAMP, Brasil. 2022.

Bertoni J, Lombardi Neto F. Conservação do solo, 3rd edn. Ícone, São Paulo, Brasil. 1990. p 355.

Yadav M, Vaishya RC. GIS-based assessment of soil loss using AHP with RUSLE model: a case study of Kaushambi-Prayagraj watershed in the Ganga Basin, U.P. (India). Water Air Soil Pollut. 2023;234:426. https://doi.org/10.1007/s11270-023-06396-4 .

Moore ID, Nieber JL. Landscape assessment of soil erosion and nonpoint source pollution. J Minnesota Acad Sci. 1991;55:18–25.

Desmet PJJ, Govers G. A GIS procedure for automatically calculating the usle LS factor on topographically complex landscape units. J Soil Water Conserv. 1996;51(5):427–33.

Funk Ch, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla Sh, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci Data. 2015;2: 150066. https://doi.org/10.1038/sdata.2015.66 .

Touma D, Martinez C. National Center for Atmospheric Research Staff (Eds). Last modified 2023-08-02 "The Climate Data Guide: CHIRPS: Climate Hazards InfraRed Precipitation with Station data (version 2).” Retrieved from https://climatedataguide.ucar.edu/climate-data/chirps-climate-hazards-infrared-precipitation-station-data-version-2 on 2023-11-23.

Huffman GJ, EF Stocker DT Bolvin EJ, Nelkin Jackson Tan. GPM IMERG Final Precipitation L3 Half Hourly 0.1-degree x 0.1-degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date]. 2019. 10.5067/GPM/IMERG/3B-HH/06.

Hurni H. Erosion-productivity-conservation systems in Ethiopia. 1985. 654–674.

Gisbert J, Ibáñez S, Moreno HE. factor K de la ecuación universal de pérdidas de suelo (USLE). Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural: Universitat Politécnica de Valencia; 2019.

Guarderas P, Smith F, Dufrene M. Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces. PLoS ONE. 2022;17(7): e0260191. https://doi.org/10.1371/journal.pone.0260191 .

Tanyaş H, Kolat Ç, Süzen ML. A new approach to estimate cover-management factor of RUSLE and validation of RUSLE model in the watershed of Kartalkaya Dam. J Hydrol. 2015;528:584–98. https://doi.org/10.1016/j.jhydrol.2015.06.048 .

Matic L. Land Cover Classification with eo-learn: Part 1, Part 2, Part 3. Published in Sentinel Hub Blog. Medium. Nov 5, 2018. Jun 9, 2019, Feb 14, 2019. https://medium.com/sentinel-hub/land-cover-classification-with-eo-learn-part-1-2471e8098195

Asuquo DE, Umoren I, Osnag F, Attai K. A Machine learning framework for length of stay minimization in healthcare emergency department. Stud Eng Technol J. 2023;10(1):1–17. https://doi.org/10.11114/set.v10i1.6372 .

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30:3149–57.

Umoh U, Asuquo D, Eyoh I, Abayomi A, Nyoho E, Vincent H. A fuzzy-based support vector regression framework for crop yield prediction. In: Sharma TK, Ahn CW, Verma OP, Panigrahi BK, editors. Soft computing: theories and applications. Advances in intelligent systems and computing, vol. 1380. Singapore: Springer; 2022.

Arias-Muñoz P, Saz MA, Escolano S. Estimación de la erosión del suelo mediante el modelo RUSLE. Caso de estudio: cuenca media alta del río Mira en los Andes de Ecuador. Investig Geogr. 2023;79:207–30. https://doi.org/10.14198/INGEO.22390 .

Download references

Acknowledgements

The authors acknowledge support from Universidad Técnica del Norte, Ibarra, Ecuador. They also thank the Sentinel Hub platform, the eo-learn library, and Google Colaboratory .

This study did not receive dedicated funding from public, commercial, or non-profit organizations.

Author information

Authors and affiliations.

Department of Software Engineering and Artificial Intelligence, Faculty of Engineering in Applied Sciences, Universidad Técnica del Norte, Avenue 17 de Julio 5-21, Ibarra, Ecuador

Fernando Garrido & Pedro Granda

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Fernando Garrido .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were by the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards .

Informed Consent

Informed consent was obtained from all participants included in the study.

Crediting Images

Imagery from Sinergise Sentinel Hub courtesy of Sentinel (Copernicus project operated by ESA) Landsat (by USGC).

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Garrido, F., Granda, P. Risk Analysis of Soil Erosion Using Remote Sensing, GIS, and Machine Learning Models in Imbabura Province, Ecuador. SN COMPUT. SCI. 5 , 824 (2024). https://doi.org/10.1007/s42979-024-03150-3

Download citation

Received : 08 March 2024

Accepted : 17 July 2024

Published : 27 August 2024

DOI : https://doi.org/10.1007/s42979-024-03150-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Soil erosion
• Remote sensing
• Land Use Land Cover (LU/LC)

Advertisement

• Find a journal
• Publish with us
• Track your research