Search

Menu
Search
in Ecology

Assessment of African grassland sustainability for livestock use by constructing a carrying capacity alert index

23 Views


Abstract

Beyond natural factors, overgrazing is emerging as a critical threat to grassland ecosystems, potentially driving degradation and unsustainable resource utilization. Grassland carrying capacity refers to the maximum number of livestock that can be sustained by available grassland biomass. Accurately assessing its sustainability is critically important, yet challenging without considering long-term trends, especially at the continental scale. Here, we develop a grassland carrying capacity alert index to assess its utilization risk, reflecting whether grassland ecosystems are currently sustainable. Using data from 2001 to 2020, we assess spatiotemporal changes in grassland carrying capacity across Africa. Our results show that increases in aboveground biomass do not keep pace with growing livestock feed demand. Moreover, the utilization level of grassland resources remains highly uneven across the continent. Approximately 51.02% of African grasslands are under high pressure, with livestock numbers exceeding carrying capacity, primarily in northern Africa. In contrast, 26.53% of grasslands, mainly in southern Africa, remain underutilized over the long term. Shifting livestock development focus to Southern Africa, complemented by cross-regional cooperation to utilize southern pastures for alleviating northern overgrazing, may balance economic growth with grassland sustainability across the continent.

Data availability

The livestock data for Africa countries from FAOSTAT database (https://www.fao.org/faostat/). The Gridded Livestock of the World dataset is available on the FAO Livestock Systems website (http://www.fao.org/livestock-systems/global-distributions/en/). NPP data for African countries are derived from the MOD17A3HGF dataset, which can be downloaded from the LAADS DAAC website (https://ladsweb.modaps.eosdis.nasa.gov/). The EAR5 dataset used for daily meteorological data can be downloaded from the ECMWF website (https://cds-beta.climate.copernicus.eu/datasets). PDSI and VPD data were extracted from the TerraClimate dataset, available for download at Climatology Lab(https://www.climatologylab.org/terraclimate.html). The percent tree cover uses the MOD44B VCF dataset, which is available at LP DAAC. The topographic data were derived from the Geomorpho90m dataset, which was published by Amatulli et al. and is publicly available on the OpenTopography website (https://portal.opentopography.org/datasets)77. Source data are provided with this paper.

Code availability

The code for estimating aboveground biomass of African grasslands is hosted on Google Earth Engine (https://code.earthengine.google.com/fd12ba602cd5eb1b85c90a9c8f95b278). Custom MATLAB scripts for analyzing trends in African grassland AGB using the Theil–Sen estimator and Mann‒Kendall method are provided in the Figshare database (https://doi.org/10.6084/m9.figshare.28590758).

References

  1. Muraina, T. O. et al. Grassland cover declined in Southern Africa but increased in other African subcontinents in early twenty-first century. Environ Monit. Assess. 195, 621 (2023).

  2. Holechek, J., Pieper, R. D. & Herbel, C. H. Range Management: Principles and Practices (Pearson, 2010).

  3. Thornton, P. K. et al. Vulnerability, climate change and livestock – opportunities and challenges for the poor. J. Semi-Arid Trop. Agric. Res. 4, (2007).

  4. Yaméogo, N. D., Nabassaga, T. & Ncube, M. Diversification and sophistication of livestock products: the case of African countries. Food Policy 49, 398–407 (2014).

    Google Scholar 

  5. Rufino, M. C. et al. Sustainable development of crop-livestock farms in Africa. Front. Agric. Sci. Eng. 8, 175–181 (2021).

    Google Scholar 

  6. Boles, O. J. C. et al. Historical ecologies of pastoralist overgrazing in Kenya: long-term perspectives on cause and effect. Hum. Ecol. 47, 419–434 (2019).

    Google Scholar 

  7. Mganga, K. Z., Nyariki, D. M., Musimba, N. K. R. & Amwata, D. A. Determinants and rates of land degradation: application of stationary time-series model to data from a semi-arid environment in Kenya. J. Arid Land 10, 1–11 (2018).

    Google Scholar 

  8. Swift, E. D. M. Stability of African pastoral ecosystems: alternate paradigms and implications for development. J. Range Manag. 41, 450–459 (1988).

    Google Scholar 

  9. Briske, D. D., Fuhlendorf, S. D. & Smeins, F. E. State-and-transition models, thresholds, and rangeland health: a synthesis of ecological concepts and perspectives. Range. Ecol. Manag. 58, 1–10 (2005).

    Google Scholar 

  10. Bengtsson, J. et al. Grasslands—more important for ecosystem services than you might think. Ecosphere 10, e02582 (2019).

  11. Morris, C. Grasslands and Climate Change (Cambridge University Press, 2019).

  12. Wang, C. & Tang, Y. A global meta-analyses of the response of multi-taxa diversity to grazing intensity in grasslands. Environ. Res. Lett. 14, 114003 (2019).

  13. Filazzola, A. et al. The effects of livestock grazing on biodiversity are multi-trophic: a meta-analysis. Ecol. Lett. 23, 1298–1309 (2020).

  14. Roe, E. M. Viewpoint: on rangeland carrying capacity. J. Range Manag. 50, 467–472 (1997).

    Google Scholar 

  15. Hobbs, N. Estimates of habitat carrying capacity incorporating explicit nutritional constraints. J. Wildl. Manag. 49, 814–822 (1985).

    Google Scholar 

  16. Johnston, P. W., McKeon, G. M. & Day, K. A. Objective “safe” grazing capacities for southwest Queensland Australia: development of a model for individual properties. Range. J. 18, 244–258 (1996).

    Google Scholar 

  17. Piipponen, J. et al. Global trends in grassland carrying capacity and relative stocking density of livestock. Global Change Biol. 28, 3902–3919 (2022).

  18. De Leeuw, J. et al. Application of the MODIS MOD 17 Net Primary Production product in grassland carrying capacity assessment. Int. J. Appl. Earth Obs. Geoinform. 78, 66–76 (2019).

    Google Scholar 

  19. Rees, W. E. Revisiting carrying capacity: area-based indicators of sustainability. Popul. Environ. 17, 195–215 (1996).

    Google Scholar 

  20. Yapi, T. S., O’Farrell, P. J., Dziba, L. E. & Esler, K. J. Alien tree invasion into a South African montane grassland ecosystem: impact of Acacia species on rangeland condition and livestock carrying capacity. Int. J. Biodiver. Sci. Ecosyst. Serv. Manag. 14, 105–116 (2018).

    Google Scholar 

  21. Rahimi, J. et al. Beyond livestock carrying capacity in the Sahelian and Sudanian zones of West Africa. Sci. Rep. 11, 22094 (2021).

  22. Meshesha, D. T., Moahmmed, M. & Yosuf, D. Estimating carrying capacity and stocking rates of rangelands in Harshin District, Eastern Somali Region, Ethiopia. Ecol. Evol. 9, 13309–13319 (2019).

  23. Petz, K. et al. Mapping and modelling trade-offs and synergies between grazing intensity and ecosystem services in rangelands using global-scale datasets and models. Global Environ. Change 29, 223–234 (2014).

  24. Wolf, J., Chen, M. & Asrar, G. R. Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010. Remote Sens. 13, 3430 (2021).

  25. Fetzel, T. et al. Quantification of uncertainties in global grazing systems assessment. Global Biogeochem. Cycles 31, 1089–1102 (2017).

  26. Wang, L., Xiao, Y., Kong, L., Wu, B. & Ouyang, Z. Spatiotemporal patterns and early-warning of grassland carrying capacity in the Qinghai-Tibet Plateau. Acta Ecol. Sin. 42, 6684–6694 (2022).

    Google Scholar 

  27. Li, X. Significant Climate Events in the World during 2002. Meteorol. Mon. 29, 28–31 (2003).

    Google Scholar 

  28. Shao, X. et al. Global major weather and climate events in 2015 and the possible causes. Meteorol. Mon. 42, 489–495 (2016).

    Google Scholar 

  29. Yin, Y., Li, D., Sun, S., Wang, G. & Ke, Z. Global major weather and climate events in 2019 and the possible causes. Meteorol. Mon. 46, 538–546 (2020).

    Google Scholar 

  30. Zhao, M. S., Heinsch, F. A., Nemani, R. R. & Running, S. W. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).

    Google Scholar 

  31. Abdi, A. M., Seaquist, J., Tenenbaum, D. E., Eklundh, L. & Ardö, J. The supply and demand of net primary production in the Sahel. Environ. Res. Lett. 9, 094003 (2014).

  32. Wang, J. et al. Changes in biomass turnover times in tropical forests and their environmental drivers from 2001 to 2012. Earths Future 9 (2021).

  33. Cox, G. W. & Waithaka, J. M. Estimating aboveground net production and grazing harvest by wildlife on tropical grassland range. Oikos 54, 60–66 (1989).

    Google Scholar 

  34. Zarei, A., Chemura, A., Gleixner, S. & Hoff, H. Evaluating the grassland NPP dynamics in response to climate change in Tanzania. Ecol. Indicat. 125, 107600 (2021).

  35. Liu, Y. et al. Assessing the dynamics of grassland net primary productivity in response to climate change at the global scale. Chin. Geogr. Sci. 29, 725–740 (2019).

    Google Scholar 

  36. Zhang, X. et al. GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method. Earth Syst. Sci. Data 16, 1353–1381 (2024).

    Google Scholar 

  37. Fritz, S. et al. Mapping global cropland and field size. Glob. Change Biol. 21, 1980–1992 (2015).

    Google Scholar 

  38. Estes, L. D. et al. Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier. Philosophical Transact. R. Soc. B Biol. Sci. 371 (2016).

  39. Vetter, S. Rangelands at equilibrium and non-equilibrium: recent developments in the debate. J. Arid Environ. 62, 321–341 (2005).

    Google Scholar 

  40. Barbehenn, R. V., Chen, Z., Karowe, D. N. & Spickard, A. C3 grasses have higher nutritional quality than C4 grasses under ambient and elevated atmospheric CO2. Glob. Change Biol. 10, 1565–1575 (2004).

    Google Scholar 

  41. Yi, S. et al. Agricultural heritage in disintegration: trends of agropastoral transhumance on the southeast Tibetan Plateau. Int. J. Sust. Dev. World Ecol. 15, 273–282 (2008).

    Google Scholar 

  42. de Leeuw, J. et al. Distribution and diversity of wildlife in northern Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 297–306 (2001).

    Google Scholar 

  43. Banoin, F. A. Fallows, forage production and nutrient transfers by livestock in Niger Fallows, forage production and nutrient transfers by livestock in Niger. Nutrient Cycling in Agroecosystems (2003).

  44. Cossins, N. J. & Upton, M. The Borana pastoral system of Southern Ethiopia. Agric. Syst. 25, 199–218 (2007).

    Google Scholar 

  45. van Wijngaarden, W. Elephants – Trees- Grass – Grazers: Relationships Between Climate, Soils, Vegetation and Large Herbivores in a Semi-Arid Savanna Ecosysteem (Tsavo, Kenya), (1985).

  46. Kavana, P. Y., Kizima, J. B. & Msanga, Y. N. Evaluation of grazing pattern and sustainability of feed resources in pastoral areas of eastern zone of Tanzania. Livest. Res. Rural Dev. 17, #5 (2005).

    Google Scholar 

  47. Wessels, K. J., Prince, S. D., Frost, P. E. & van Zyl, D. Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sens. Environ. 91, 47–67 (2004).

    Google Scholar 

  48. Mganga, K. Z. et al. Morphoecological characteristics of grasses used to restore degraded semi-arid African rangelands. Ecol. Solut. Evid. 2, e12078 (2021).

  49. Hawkins, H. J. A global assessment of Holistic Planned Grazing™ compared with season-long, continuous grazing: meta-analysis findings. Afr. J. Range Forage Sci. 34, 65–75 (2017).

    Google Scholar 

  50. Crawford, C. L., Volenec, Z. M., Sisanya, M., Kibe, R. & Rubenstein, D. Behavioral and Ecological Implications of Bunched, Rotational Cattle Grazing in East African Savanna Ecosystem. Rangel. Ecol. Manag. 72, 204–209 (2019).

    Google Scholar 

  51. Chonco, N., Slotow, R., Tsvuura, Z. & Nkuna, S. Ecosystem Resilience of a South African Mesic Grassland with Change from Rotational to Continuous Grazing. Diversity-Basel 15 (2023).

  52. Liang, C., Michalk, D. L. & Millar, G. D. The ecology and growth patterns of Cleistogenes species in degraded grasslands of eastern Inner Mongolia, China. J. Appl. Ecol. 39, 584–594 (2002).

    Google Scholar 

  53. Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).

    Google Scholar 

  54. Fensholt, R. et al. Greenness in semi-arid areas across the globe 1981-2007 – an Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ. 121, 144–158 (2012).

    Google Scholar 

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

    Google Scholar 

  56. Pan, S., Dangal, S., Tao, B., Yang, J. & Tian, H. Recent patterns of terrestrial net primary production in Africa influenced by multiple environmental changes. Ecosyst. Health Sustain. 1, 18 (2015).

    Google Scholar 

  57. O’Connor, T. G., Puttick, J. R. & Hoffman, M. T. Bush encroachment in southern Africa: changes and causes. Afr. J. Range Forage Sci. 31, 67–88 (2014).

    Google Scholar 

  58. Tong, L. et al. Relative effects of climate variation and human activities on grassland dynamics in Africa from 2000 to 2015. Ecol. Inform. 53, 100979 (2019).

  59. Miehe, S., Kluge, J., von Wehrden, H. & Retzer, V. Long-term degradation of Sahelian rangeland detected by 27 years of field study in Senegal. J. Appl. Ecol. 47, 692–700 (2010).

    Google Scholar 

  60. Yan, N. et al. Assessment of the grassland carrying capacity for winter-spring period in Mongolia. Ecol. Indicat. 146, 109868 (2023).

  61. Hui, D. & Jackson, R. B. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. N. Phytol. 169, 85–93 (2006).

    Google Scholar 

  62. Jon, L. et al. Contributions of woody and herbaceous vegetation to tropical savanna ecosystem productivity: a quasi-global estimate. Tree Physiol. 451, 451–68 (2008).

  63. Le Brocque, A. F., Goodhew, K. & Cockfield, G. Retaining trees in a grazing landscape: Impacts on ground cover in sheep-grazing agro-ecosystems in southern Queensland. Veg Futures 08 Conference Proceedings, 30 (2008).

  64. George, M. & Lyle, D. Stocking Rate and Carrying Capacity (Ecology and Management of Grazing, an Online Course, 2009).

  65. Wu, T., Sang, S., Wang, S., Yang, Y. & Li, M. Remote sensing assessment and spatiotemporal variations analysis of ecological carrying capacity in the Aral Sea Basin. Sci. Total Environ. 735, 139562 (2020).

  66. Mann, H. B. Nonparametric test against trend. Econometrica 13, 245–259 (1945).

    Google Scholar 

  67. Kendall, M. G. Rank correlation measures. Charles Grifn London (1975).

  68. Yue, S., Pilon, P. & Cavadias, G. Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 259, 254–271 (2002).

    Google Scholar 

  69. Liu, X. et al. Air pollution in Germany: Spatio-temporal variations and their driving factors based on continuous data from 2008 to 2018. Environ. Pollut. 276, 116732 (2021).

  70. Liu, Y. L. et al. A graded index for evaluating precipitation heterogeneity in China. J. Geogr. Sci. 26, 673–693 (2016).

    Google Scholar 

  71. Hussien, K., Kebede, A., Mekuriaw, A., Beza, S. A. & Erena, S. H. Spatiotemporal trends of NDVI and its response to climate variability in the Abbay River Basin, Ethiopia. Heliyon 9, e14113 (2023).

  72. Räsänen, M. et al. Carbon balance of a grazed savanna grassland ecosystem in South Africa. Biogeosciences 14, 1039–1054 (2017).

    Google Scholar 

  73. Yan, Z. Y. et al. Global degradation trends of grassland and their driving factors since 2000. Int. J. Digit. Earth 16, 1661–1684 (2023).

    Google Scholar 

  74. Yang, T. et al. A large forage gap in forage availability in traditional pastoral regions in China. Fundamental Res. 3, 188–200 (2023).

    Google Scholar 

  75. Zhang, G., Zhang, Y., Dong, J. & Xiao, X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc. Natl. Acad. Sci. USA 110, 4309–4314 (2013).

    Google Scholar 

  76. Elliott, A. C. & Woodward, W. A. IBM SPSS by Example: A Practical Guide to Statistical Data Analysis (IBM SPSS by Example: A Practical Guide to Statistical Data Analysis, 2015).

  77. Amatulli, G., McInerney, D., Sethi, T., Strobl, P. & Domisch, S. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Sci. Data 7, 162 (2020).

Download references

Acknowledgements

This research was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19030202); Key Program of Natural Science Foundation of Henan Province, China (252300421288); the Key Program of the Joint Fund of the National Natural Science Foundation of China (U21A2014); the Major Special Project of High Resolution Earth Observation System (80-Y50G19-9001-22/23) and Henan Province Science and Technology Research Project (252102320235). We are grateful to all organizations that share data.

Author information

Authors and Affiliations

  1. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, State Key Laboratory of Spatial Datum, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, China

    Shiqi Yu, Xiwang Zhang, Yansui Liu, Yang Liu, Yuhui Cheng, Mengwei Chen, Chengqiang Zhang & Yuanyuan Jiang

  2. State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Nana Yan & Weiwei Zhu

  3. Henan Remote Sensing Institute, Zhengzhou, China

    Xiumei Yu

Authors

  1. Shiqi Yu
    View author publications

    Search author on:PubMed Google Scholar

  2. Xiwang Zhang
    View author publications

    Search author on:PubMed Google Scholar

  3. Yansui Liu
    View author publications

    Search author on:PubMed Google Scholar

  4. Nana Yan
    View author publications

    Search author on:PubMed Google Scholar

  5. Weiwei Zhu
    View author publications

    Search author on:PubMed Google Scholar

  6. Yang Liu
    View author publications

    Search author on:PubMed Google Scholar

  7. Yuhui Cheng
    View author publications

    Search author on:PubMed Google Scholar

  8. Xiumei Yu
    View author publications

    Search author on:PubMed Google Scholar

  9. Mengwei Chen
    View author publications

    Search author on:PubMed Google Scholar

  10. Chengqiang Zhang
    View author publications

    Search author on:PubMed Google Scholar

  11. Yuanyuan Jiang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

X.Z., YS.L., and S.Y. designed research, performed research, analyzed data, wrote the paper; N.Y. and W.Z. designed research, analyzed data, and revised the manuscript; Y.L., M.C., and Y.C. discussed the results and revised the manuscript; X.Y., C.Z., and Y.J. performed research and analyzed data.

Corresponding authors

Correspondence to
Xiwang Zhang, Yansui Liu, Nana Yan or Weiwei Zhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Kevin Kirkman, Jiaguo Qi, Dandan Xu, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information

Peer Review file

Reporting Summary

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Yu, S., Zhang, X., Liu, Y. et al. Assessment of African grassland sustainability for livestock use by constructing a carrying capacity alert index.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-68084-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41467-025-68084-7

Source: Ecology - nature.com

Effect of microhabitat variability on restoration success of swamp willow Salix myrtilloides L. population

Predators can facilitate herbivory in nutrient-limited marine ecosystems

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close