Skip to main content

Thank you for visiting 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.

The global cropland-sparing potential of high-yield farming


The global expansion of cropland exerts substantial pressure on natural ecosystems and is expected to continue with population growth and affluent demand. Yet earlier studies indicated that crop production could be more than doubled if attainable crop yields were achieved on present cropland. Here we show on the basis of crop modelling that closing current yield gaps by spatially optimizing fertilizer inputs and allocating 16 major crops across global cropland would allow reduction of the cropland area required to maintain present production volumes by nearly 50% of its current extent. Enforcing a scenario abandoning cropland in biodiversity hotspots and uniformly releasing 20% of cropland area for other landscape elements would still enable reducing the cropland requirement by almost 40%. As a co-benefit, greenhouse gas emissions from fertilizer and paddy rice, as well as irrigation water requirements, are likely to decrease with a reduced area of cultivated land, while global fertilizer input requirements remain unchanged. Spared cropland would provide space for substantial carbon sequestration in restored natural vegetation. Only targeted sparing of biodiversity hotspots supports species with small-range habitats, while biodiversity would hardly profit from a maximum land-sparing approach.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Schematic of the study design.
Fig. 2: Global extent of annual cropland in the reference period and the two land-sparing scenarios.
Fig. 3: Proportion of each 5′ × 5′ pixel covered by cropland cultivated and cropland fractions released in the two land-sparing scenarios.
Fig. 4: Relative changes in key agricultural externalities following optimization of area of cropland for the two scenarios.

Data availability

Datasets required for reproducing key results of the cropland allocation model are available via

Code availability

The code required for reproducing key results of the cropland allocation model is available from


  1. 1.

    van der Velde, M. et al. African crop yield reductions due to increasingly unbalanced nitrogen and phosphorus consumption. Glob. Change Biol. 20, 1278–1288 (2014).

    Google Scholar 

  2. 2.

    MacDonald, G. K., Bennett, E. M., Potter, P. A. & Ramankutty, N. Agronomic phosphorus imbalances across the world’s croplands. Proc. Natl Acad. Sci. USA 108, 3086–3091 (2011).

    CAS  Google Scholar 

  3. 3.

    Siebert, S. & Döll, P. Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J. Hydrol. 384, 198–217 (2010).

    Google Scholar 

  4. 4.

    Carlson, K. M. et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Change 7, 63–68 (2017).

    CAS  Google Scholar 

  5. 5.

    Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).

    Google Scholar 

  6. 6.

    Balmford, A. & Green, R. How to spare half a planet. Nature 552, 175 (2017).

  7. 7.

    Ewers, R. M., Scharlemann, J. P. W., Balmford, A. & Green, R. E. Do increases in agricultural yield spare land for nature? Glob. Change Biol. 15, 1716–1726 (2009).

    Google Scholar 

  8. 8.

    Phalan, B. et al. How can higher-yield farming help to spare nature? Science 351, 450–451 (2016).

    CAS  Google Scholar 

  9. 9.

    Salles, J.-M., Teillard, F., Tichit, M. & Zanella, M. Land sparing versus land sharing: an economist’s perspective. Reg. Environ. Change 17, 1455–1465 (2017).

    Google Scholar 

  10. 10.

    Wilson, E. O. Half-Earth: Our Planet’s Fight for Life (Norton, 2016).

  11. 11.

    Bodirsky, B. L. et al. Global food demand scenarios for the 21st century. PLoS ONE 10, e0139201 (2015).

    Google Scholar 

  12. 12.

    Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).

    Google Scholar 

  13. 13.

    Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).

    CAS  Google Scholar 

  14. 14.

    Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).

    Google Scholar 

  15. 15.

    Erb, K.-H. et al. Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun. 7, 11382 (2016).

    CAS  Google Scholar 

  16. 16.

    Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).

    CAS  Google Scholar 

  17. 17.

    Balkovič, J. et al. Global wheat production potentials and management flexibility under the representative concentration pathways. Glob. Planet. Change 122, 107–121 (2014).

    Google Scholar 

  18. 18.

    Mauser, W. et al. Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nat. Commun. 6, 8946 (2015).

    CAS  Google Scholar 

  19. 19.

    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    CAS  Google Scholar 

  20. 20.

    Koh, L. P., Koellner, T. & Ghazoul, J. Transformative optimisation of agricultural land use to meet future food demands. PeerJ 1, e188 (2013).

    Google Scholar 

  21. 21.

    Davis, K. F., Rulli, M. C., Seveso, A. & D’Odorico, P. Increased food production and reduced water use through optimized crop distribution. Nat. Geosci. 10, 919–924 (2017).

    CAS  Google Scholar 

  22. 22.

    Balmford, A., Green, R. & Phalan, B. Land for food & land for nature? Daedalus 144, 57–75 (2015).

    Google Scholar 

  23. 23.

    Balmford, A. et al. The environmental costs and benefits of high-yield farming. Nat. Sustain. 1, 477–485 (2018).

    Google Scholar 

  24. 24.

    Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).

    Google Scholar 

  25. 25.

    Williams, J. R. The erosion–productivity impact calculator (EPIC) model: a case history. Phil. Trans. R. Soc. Lond. B 329, 421–428 (1990).

    Google Scholar 

  26. 26.

    Izaurralde, R. C., Williams, J. R., McGill, W. B., Rosenberg, N. J. & Jakas, M. C. Q. Simulating soil C dynamics with EPIC: model description and testing against long-term data. Ecol. Modell. 192, 362–384 (2006).

    Google Scholar 

  27. 27.

    Feniuk, C., Balmford, A. & Green, R. E. Land sparing to make space for species dependent on natural habitats and high nature value farmland. Proc. R. Soc. B 286, 20191483 (2019).

    Google Scholar 

  28. 28.

    Schulte, L. A. et al. Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn–soybean croplands. Proc. Natl Acad. Sci. USA 114, 11247–11252 (2017).

    CAS  Google Scholar 

  29. 29.

    Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).

    Google Scholar 

  30. 30.

    Ellis, E. C. Sharing the land between nature and people. Science 364, 1226–1228 (2019).

    CAS  Google Scholar 

  31. 31.

    Verburg, P. H., Mertz, O., Erb, K.-H., Haberl, H. & Wu, W. Land system change and food security: towards multi-scale land system solutions. Curr. Opin. Environ. Sustain. 5, 494–502 (2013).

    Google Scholar 

  32. 32.

    Puma, M. J., Bose, S., Chon, S. Y. & Cook, B. I. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 (2015).

    Google Scholar 

  33. 33.

    Alston, J. M., Babcock, B. A. & Pardey, P. G. The Shifting Patterns of Agricultural Production and Productivity Worldwide (Midwest Agribusiness Trade Research and Information Center, 2010).

  34. 34.

    Müller, D. et al. Regime shifts limit the predictability of land-system change. Glob. Environ. Change 28, 75–83 (2014).

    Google Scholar 

  35. 35.

    Kastner, T., Erb, K.-H. & Haberl, H. Rapid growth in agricultural trade: effects on global area efficiency and the role of management. Environ. Res. Lett. 9, 034015 (2014).

    Google Scholar 

  36. 36.

    Barzman, M. et al. Eight principles of integrated pest management. Agron. Sustain. Dev. 35, 1199–1215 (2015).

    Google Scholar 

  37. 37.

    Roy, E. D. et al. The phosphorus cost of agricultural intensification in the tropics. Nat. Plants 2, 16043 (2016).

    CAS  Google Scholar 

  38. 38.

    Jägermeyr, J. et al. Water savings potentials of irrigation systems: global simulation of processes and linkages. Hydrol. Earth Syst. Sci. 19, 3073–3091 (2015).

    Google Scholar 

  39. 39.

    Sterling, S. M., Ducharne, A. & Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Change 3, 385–390 (2013).

    CAS  Google Scholar 

  40. 40.

    Folberth, C., Yang, H., Gaiser, T., Abbaspour, K. C. & Schulin, R. Modeling maize yield responses to improvement in nutrient, water and cultivar inputs in sub-Saharan Africa. Agric. Syst. 119, 22–34 (2013).

    Google Scholar 

  41. 41.

    West, P. C. et al. Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land. Proc. Natl Acad. Sci. USA 107, 19645–19648 (2010).

    CAS  Google Scholar 

  42. 42.

    Leclere, D. et al. Towards Pathways Bending the Curve of Terrestrial Biodiversity Trends within the 21st Century (IIASA, 2018).

  43. 43.

    Visconti, P. et al. Projecting global biodiversity indicators under future development Scenarios. Conserv. Lett. 9, 5–13 (2016).

    Google Scholar 

  44. 44.

    Phalan, B. T. What have we learned from the land sparing–sharing model? Sustainability 10, 1760 (2018).

    Google Scholar 

  45. 45.

    Tscharntke, T., Klein, A. M., Kruess, A., Steffan‐Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol. Lett. 8, 857–874 (2005).

    Google Scholar 

  46. 46.

    Stehfest, E. et al. Key determinants of global land-use projections. Nat. Commun. 10, 2166 (2019).

    Google Scholar 

  47. 47.

    Schmitz, C. et al. Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45, 69–84 (2014).

    Google Scholar 

  48. 48.

    Schmidt-Traub, G., Obersteiner, M. & Mosnier, A. Fix the broken food system in three steps. Nature 569, 181–183 (2019).

    CAS  Google Scholar 

  49. 49.

    FAOSTAT Statistical Database (FAO, 2016).

  50. 50.

    Müller, C., Bondeau, A., Lotze-Campen, H., Cramer, W. & Lucht, W. Comparative impact of climatic and nonclimatic factors on global terrestrial carbon and water cycles. Glob. Biogeochem. Cycles 20, GB4015 (2006).

    Google Scholar 

  51. 51.

    Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).

    Google Scholar 

  52. 52.

    Balkovič, J. et al. Impacts and uncertainties of +2 °C of climate change and soil degradation on European crop calorie supply. Earth’s Future 6, 373–395 (2018).

    Google Scholar 

  53. 53.

    Balkovič, J. et al. Pan-European crop modelling with EPIC: implementation, up-scaling and regional crop yield validation. Agric. Syst. 120, 61–75 (2013).

    Google Scholar 

  54. 54.

    Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).

    CAS  Google Scholar 

  55. 55.

    Harmonized World Soil Database Version 1.2 (FAO, 2012).

  56. 56.

    GTOPO30 - Global Topographic 30 Arc-Second Digital Elevation Model (USGS, 2002).

  57. 57.

    Skalský, R. et al. GEO-BENE Global Database for Bio-Physical Modeling (GEOBENE project, 2008);

  58. 58.

    Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric. For. Meteorol. 200, 233–248 (2015).

    Google Scholar 

  59. 59.

    Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

    Google Scholar 

  60. 60.

    Global Spatially-Disaggregated Crop Production Statistics Data for 2005 Version 3.2 (IFPRI and IIASA, 2016).

  61. 61.

    Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

    Google Scholar 

  62. 62.

    Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).

    Google Scholar 

  63. 63.

    Porwollik, V. et al. Spatial and temporal uncertainty of crop yield aggregations. Eur. J. Agron. 88, 10–21 (2017).

    Google Scholar 

  64. 64.

    de Albuquerque, F. S. & Gregory, A. The geography of hotspots of rarity-weighted richness of birds and their coverage by natura 2000. PLoS ONE 12, e0174179 (2017).

    Google Scholar 

  65. 65.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).

    Google Scholar 

  66. 66.

    Batjes, N. H. Global Distribution of Soil Phosphorus Retention Potential (ISRIC, 2011).

  67. 67.

    Cafaro La Menza, N., Monzon, J. P., Specht, J. E. & Grassini, P. Is soybean yield limited by nitrogen supply? Field Crops Res. 213, 204–212 (2017).

    Google Scholar 

  68. 68.

    Crop Nutrient Tool | USDA PLANTS (USDA, accessed March 2016).

  69. 69.

    Köble. R. The Global Nitrous Oxide Calculator—GNOC—Online Tool Manual Version 1.2.4. (JRC, 2014).

  70. 70.

    Liu, J. et al. A high-resolution assessment on global nitrogen flows in cropland. Proc. Natl Acad. Sci. USA 107, 8035–8040 (2010).

    CAS  Google Scholar 

  71. 71.

    Bouwman, L. et al. Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period. Proc. Natl Acad. Sci. USA 110, 20882–20887 (2013).

    CAS  Google Scholar 

  72. 72.

    Jägermeyr, J. et al. Integrated crop water management might sustainably halve the global food gap. Environ. Res. Lett. 11, 025002 (2016).

    Google Scholar 

  73. 73.

    Qin, Y. et al. Flexibility and intensity of global water use. Nat. Sustain. 2, 515–523 (2019).

    Google Scholar 

  74. 74.

    Tubiello, F. N. et al. The FAOSTAT database of greenhouse gas emissions from agriculture. Environ. Res. Lett. 8, 015009 (2013).

    Google Scholar 

  75. 75.

    The IUCN Red List of Threatened Species (IUCN, accessed April 2018).

  76. 76.

    Bontemps, S. et al. Consistent global land cover maps for climate modelling communities: current achievements of the ESA’s land cover CCI. In Proc. ESA Living Planet Symposium (ed. Ouwehand, L.) 9–13 (ESA SP-722, 2013);

  77. 77.

    R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008).

  78. 78.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  79. 79.

    Perpinan, O. & Hijmans, R. rasterVis R package Version 0.41 (2016);

Download references


C.F., N.K., J.B., R.S., P.C., I.A.J., J.P. and M.O. were supported by European Research Council Synergy grant ERC-2013-SynG-610028 Imbalance-P. Part of the work by C.F. was supported by a research fellowship of the Center for Advanced Studies at Ludwig Maximilian University Munich. P.C. received support from the ANR CLAND Institute of Convergence (16-CONV-0003). We gratefully acknowledge the provision of threatened species data by IUCN and the provision of land-cover data by the ESA-CCI Land Cover project.

Author information




C.F., N.K. and M.O. designed the study; C.F. and N.K. performed central analyses; J.B., R.S. and P.V. contributed models and data; C.F. wrote an initial draft; C.F., N.K., J.B., R.S., P.V., P.C., I.A.J., J.P. and M.O. contributed substantially to the interpretation of the results and revisions of the manuscript.

Corresponding author

Correspondence to Christian Folberth.

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 Figs. 1–20, Tables 1–4, Methods 1–4, Text 1–4 and Data 1.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Folberth, C., Khabarov, N., Balkovič, J. et al. The global cropland-sparing potential of high-yield farming. Nat Sustain 3, 281–289 (2020).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing