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.

Landscape complexity and US crop production


Agricultural expansion and intensification have simplified Earth’s landscapes, thereby adversely affecting the biodiversity and ecosystem services that support agricultural production. Field-scale research suggests that increased landcover complexity can improve crop productivity, but less is known about how complexity and crop productivity interact at broader landscape scales. This study evaluates the relationship between landscape complexity and crop yields for counties in the conterminous United States from 2008 to 2018. Our results suggest that the number and quantity of landcover categories on a landscape has a stronger influence on yields than how these landcover categories are arranged on the landscape. Specifically, increased landcover diversity is associated with yield increases for corn and wheat of more than 10%—an effect strength similar to the impact of seasonal precipitation and soil suitability. Notably, landscape configurations that are both moderately complex and also highly diverse are associated with yield increases of more than 20% for corn and wheat. Our findings suggest that increasing the complexity of landcover may provide a way to improve crop productivity in the United States without further extensification or intensification of agriculture.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Compositional and configurational complexity in the United States.
Fig. 2: Response of yield to compositional complexity.
Fig. 3: Response of yield to configurational complexity.
Fig. 4: Response of yield to composition and configuration interactions.
Fig. 5: Response of yield to bioclimatic controls.

Data availability

All data used in this study are publicly available at the sources referenced within the Methods. The compiled dataset is available from the authors upon request. Sources include:,,,,, and A descriptive exploratory data analysis of the compiled dataset used in this study is available at

Code availability

Code employed in this study for model evaluation is available at or from the authors upon request.


  1. 1.

    Landis, D. A. Designing agricultural landscapes for biodiversity-based ecosystem services. Basic Appl. Ecol. 18, 1–12 (2017).

    Google Scholar 

  2. 2.

    Aguilar, J. et al. Crop species diversity changes in the United States: 1978–2012. PLoS ONE 10, e0136580 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Census of Agriculture (USDA National Agricultural Statistics Service, 2017);

  4. 4.

    Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).

    PubMed  Google Scholar 

  5. 5.

    Meehan, T. D., Werling, B. P., Landis, D. A. & Gratton, C. Agricultural landscape simplification and insecticide use in the Midwestern United States. Proc. Natl Acad. Sci. USA 108, 11500–11505 (2011).

    ADS  CAS  PubMed  Google Scholar 

  6. 6.

    Tiemann, L. K., Grandy, A. S., Atkinson, E. E., Marin-Spiotta, E. & McDaniel, M. D. Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol. Lett. 18, 761–771 (2015).

    CAS  PubMed  Google Scholar 

  7. 7.

    Abson, D. J., Fraser, E. D. & Benton, T. G. Landscape diversity and the resilience of agricultural returns: a portfolio analysis of land-use patterns and economic returns from lowland agriculture. Agric. Food Secur. 2, 2 (2013).

    Google Scholar 

  8. 8.

    Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Ojha, S. & Dimov, L. Variation in the diversity-productivity relationship in young forests of the eastern United States. PLoS ONE 12, e0187106 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Smith, R. G., Gross, K. L. & Robertson, G. P. Effects of crop diversity on agroecosystem function: crop yield response. Ecosystems 11, 355–366 (2008).

    Google Scholar 

  11. 11.

    Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 115, E7863–E7870 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Bastian, O., Grunewald, K., Syrbe, R. U., Walz, U. & Wende, W. Landscape services: the concept and its practical relevance. Landsc. Ecol. 29, 1463–1479 (2014).

    Google Scholar 

  13. 13.

    Winfree, R. et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales. Science 359, 791–793 (2018).

    ADS  CAS  PubMed  Google Scholar 

  14. 14.

    Duarte, G. T., Santos, P. M., Cornelissen, T. G., Ribeiro, M. C. & Paglia, A. P. The effects of landscape patterns on ecosystem services: meta-analyses of landscape services. Landsc. Ecol. 33, 1247–1257 (2018).

    Google Scholar 

  15. 15.

    Li, C. et al. Crop diversity for yield increase. PLoS ONE 4, e8049 (2009).

    ADS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Swinton, S. M., Lupi, F., Robertson, G. P. & Hamilton, S. K. Ecosystem services and agriculture: cultivating agricultural ecosystems for diverse benefits. Ecol. Econ. 64, 245–252 (2007).

    Google Scholar 

  17. 17.

    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    ADS  CAS  PubMed  Google Scholar 

  18. 18.

    Burchfield, E. K., Nelson, K. S. & Spangler, K. The impact of agricultural landscape diversification on US crop production. Agric. Ecosyst. Environ. 285, 106615 (2019).

    Google Scholar 

  19. 19.

    Galpern, P., Vickruck, J., Devries, J. H. & Gavin, M. P. Landscape complexity is associated with crop yields across a large temperate grassland region. Agric. Ecosyst. Environ. 290, 106724 (2020).

    Google Scholar 

  20. 20.

    Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol. Evol. 4, 3514–3524 (2014).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Burke, M. & Emerick, K. Adaptation to climate change: evidence from US agriculture. Am. Econ. J. Econ. Pol. 8, 106–140 (2016).

    Google Scholar 

  22. 22.

    Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).

    ADS  Google Scholar 

  23. 23.

    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    ADS  CAS  PubMed  Google Scholar 

  24. 24.

    Schauberger, B., Rolinski, S. & Müller, C. A network-based approach for semi-quantitative knowledge mining and its application to yield variability. Environ. Res. Lett. 11, 123001 (2016).

    ADS  Google Scholar 

  25. 25.

    Burchfield, E., Matthews-Pennanen, N., Stoebner, J. & Lant, C. Changing yields in the central United States under climate and technological change. Clim. Change 159, 329–346 (2019).

    ADS  Google Scholar 

  26. 26.

    Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    ADS  CAS  PubMed  Google Scholar 

  27. 27.

    Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).

    ADS  Google Scholar 

  28. 28.

    Chaplin‐Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta‐analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).

    PubMed  Google Scholar 

  29. 29.

    Grab, H., Danforth, B., Poveda, K. & Loeb, G. Landscape simplification reduces classical biological control and crop yield. Ecol. Appl. 28, 348–355 (2018).

    PubMed  Google Scholar 

  30. 30.

    Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094 (2019).

    PubMed  Google Scholar 

  31. 31.

    Lark, T. J., Spawn, S. A., Bougie, M. & Gibbs, H. K. Cropland expansion in the United States produces marginal yields at high costs to wildlife. Nat. Commun. 11, 4295 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Finney, D. M. & Kaye, J. P. Functional diversity in cover crop polycultures increases multifunctionality of an agricultural system. J. Appl. Ecol. 54, 509–517 (2017).

    Google Scholar 

  33. 33.

    Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2, 284–293 (2020).

    Google Scholar 

  34. 34.

    Tscharntke, T. et al. Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol. Lett. 8, 857–874 (2012).

    Google Scholar 

  35. 35.

    Swift, M. J., Izac, A.-M. N. & van Noordwijk, M. Biodiversity and ecosystem services in agricultural landscapes—are we asking the right questions? Agric. Ecosyst. Environ. 104, 113–134 (2004).

    Google Scholar 

  36. 36.

    CropScrape—Cropland Data Layer (USDA National Agricultural Statistics Service, 2018);

  37. 37.

    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  PubMed  Google Scholar 

  38. 38.

    Albrecht, M. et al. The effectiveness of flower strips and hedgerows on pest control, pollination services and crop yield: a quantitative synthesis. Ecol. Lett. 23, 1488–1498 (2020).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Liang, X. Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. USA 114, E2285–E2292 (2017).

    CAS  PubMed  Google Scholar 

  40. 40.

    Brandes, E. et al. Subfield profitability analysis reveals an economic case for cropland diversification. Environ. Res. Lett. 11, 014009 (2016).

    ADS  Google Scholar 

  41. 41.

    Capmourteres, V. et al. Precision conservation meets precision agriculture: a case study from southern Ontario. Agric. Syst. 167, 176–185 (2018).

    Google Scholar 

  42. 42.

    Census of Agriculture (USDA National Agricultural Statistics Service, 2019);

  43. 43.

    Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, GB1003 (2008).

    Google Scholar 

  45. 45.

    PRISM Climate Data (PRISM Climate Group, 2004)

  46. 46.

    Miller, P., Lanier, W. & Brandt, S. Using Growing Degree Days to Predict Plant Stages (Montana State University, 2001);

  47. 47.

    agweather connection (Mesonet, 2007);

  48. 48.

    Corn Growing Degree Days (NDAWN: North Dakota Agricultural Weather Network, 2017);

  49. 49.

    Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous United States (US Department of Agriculture, Natural Resources Conservation Service, 2014);

  50. 50.

    Dobos, R. R., Sinclair, H. R., Jr & Robotham, M. P. User Guide for the National Commodity Crop Productivity Index (NCCPI, 2012).

  51. 51.

    Plexida, S. G., Sfougaris, A. I., Ispikoudis, I. P. & Papanastasis, V. P. Selecting landscape metrics as indicators of spatial heterogeneity—a comparison among Greek landscapes. Int. J. Appl. Earth Obs. Geoinf. 26, 26–35 (2014).

    ADS  Google Scholar 

  52. 52.

    Schindler, S., Poirazidis, K. & Wrbka, T. Towards a core set of landscape metrics for biodiversity assessments: a case study from Dadia National Park, Greece. Ecol. Indic. 8, 502–514 (2008).

    Google Scholar 

  53. 53.

    Turner, M. G. Spatial and temporal analysis of landscape patterns. Landsc. Ecol. 4, 21–30 (1990).

    Google Scholar 

  54. 54.

    Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399 (2004).

    Google Scholar 

  55. 55.

    Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1–10 (2019).

    Google Scholar 

  56. 56.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017);

  57. 57.

    Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol. 71, 319–392 (2009).

    MathSciNet  MATH  Google Scholar 

  58. 58.

    Blanc, E. & Schlenker, W. The use of panel models in assessments of climate impacts on agriculture. Rev. Environ. Econ. Policy 11, 258–279 (2017).

    Google Scholar 

  59. 59.

    Level III Ecoregions of the Continental United States (US Environmental Protection Agency, 2011);

  60. 60.

    Bakka, H. et al. Spatial modeling with R-INLA: a review. Wiley Interdiscip. Rev. Comput. Stat. 10, e1443 (2018).

    MathSciNet  Google Scholar 

  61. 61.

    2018 Cartographic Boundary Files [data set] (US Census Bureau, 2018);

Download references


This work was supported in part by US Department of Agriculture National Institute of Food and Agriculture grant number 2020-67019-31157. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Department of Agriculture.

Author information




Both authors conceptualized and wrote the paper. E.K.B. led construction of the dataset used for analyses, including processing of raw climate and landscape data. K.S.N. led the modelling analyses.

Corresponding author

Correspondence to Katherine S. Nelson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Matteo Dainese, Poggi Sylvain and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Fig. 1–4 and Table 1.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nelson, K.S., Burchfield, E.K. Landscape complexity and US crop production. Nat Food 2, 330–338 (2021).

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