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Double cropping and cropland expansion boost grain production in Brazil

Nature Food volume 2, pages 264–273 (2021)Cite this article

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Abstract

Brazilian grain production increased more than fourfold from 1980 to 2016. The grain boom was achieved primarily by soybean–corn double cropping and cropland expansion—both show changing spatiotemporal patterns since the 1980s. Here, we quantified the contributions of these two strategies to corn and soybean production in Brazil using municipality-level data from 1980 to 2016. We found the contribution of double cropping to the grain boom steadily increased to 35% and the largest driving force was the increasing demand for grain export. While double cropping dominated the conventional agricultural regions, cropland expansion was still the major strategy in agricultural frontiers such as the Centre-West and Matopiba. The implementation of double cropping offset the equivalent of 76.7 million ha of Brazilian arable land for grain production from 2003 to 2016. Double cropping in Brazil has the potential to help alleviate land burdens in other pantropical countries with increasing global food demand.

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Fig. 1: An overview of the historical corn and soybean production conditions in Brazil.
Fig. 2: Harvested area density of soybean, second-season corn and first-season corn at the county level in Brazil in the selected years 2003, 2007, 2012 and 2016.
Fig. 3: Regional proportions of grain production and area in Brazil from 1980 to 2016.
Fig. 4: Area changes of corn and soybean attributed to cropland expansion and double cropping.
Fig. 5: Regional area changes of corn and soybean attributed to cropland expansion and double cropping.
Fig. 6: Cropping frequency of soybean and corn systems in the key agricultural regions of Brazil.
Fig. 7: Trends of the harvested area fraction of the first-season crops (soybean and first-season corn) in the key agricultural regions of Brazil from 2003 to 2016.
Fig. 8: Contribution of seven socioeconomic and cropping structure drivers to the national double-cropping development during the four periods of 1980–2003, 2003–2007, 2007–2012 and 2012–2016.

Data availability

The data that support the findings of this study are available at https://www.ibge.gov.br/.

Code availability

The codes used for data processing, analysis and visualization during the current study are available from the corresponding author on reasonable request.

References

  1. FAOSTAT Countries by Commodity (Food and Agriculture Organization of the United Nations, accessed 29 November 2019); http://www.fao.org/faostat/en/#rankings/countries_by_commodity

  2. FAOSTAT Crops (Food and Agriculture Organization of the United Nations, accessed 29 November 2019); http://www.fao.org/faostat/en/#data/QC

  3. Freitas, R. E. Expansion of agricultural areas in Brazil from 1994 to 2013: soybeans versus corn versus cotton. Organ. Rurais Agroindustriais 19, 219–232 (2017).

    Article  Google Scholar 

  4. Cohn, A. S., VanWey, L. K., Spera, S. A. & Mustard, J. F. Cropping frequency and area response to climate variability can exceed yield response. Nat. Clim. Change 6, 601–604 (2016).

    Article  ADS  Google Scholar 

  5. Zalles, V. et al. Near doubling of Brazil’s intensive row crop area since 2000. Proc. Natl Acad. Sci. USA 116, 428–435 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Ramalho, M. A. P., Dias, L. A., dos, S. & Carvalho, B. L. Contributions of plant breeding in Brazil: progress and perspectives. Crop Breed. Appl. Biotechnol. 12, 111–120 (2012).

    Article  Google Scholar 

  7. Rada, N. & Valdes, C. Policy, Technology, and Efficiency of Brazilian Agriculture Economic Research Report No. 137 (USDA Economic Research Service, 2012).

  8. Strassburg, B. B. N. et al. Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol. 1, 0099 (2017).

    Article  Google Scholar 

  9. Karstensen, J., Peters, G. P. & Andrew, R. M. Attribution of CO2 emissions from Brazilian deforestation to consumers between 1990 and 2010. Environ. Res. Lett. 8, 024005 (2013).

    Article  ADS  CAS  Google Scholar 

  10. Soares-Filho, B. et al. Role of Brazilian Amazon protected areas in climate change mitigation. Proc. Natl Acad. Sci. USA 107, 10821–10826 (2010).

    Article  ADS  CAS  PubMed  Google Scholar 

  11. Zhang, Y. et al. Dramatic cropland expansion in Myanmar following political reforms threatens biodiversity. Sci. Rep. 8, 16558 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  12. Kehoe, L. et al. Biodiversity at risk under future cropland expansion and intensification. Nat. Ecol. Evol. 1, 1129–1135 (2017).

    Article  PubMed  Google Scholar 

  13. Noojipady, P. et al. Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome. Environ. Res. Lett. 12, 025004 (2017).

    Article  ADS  Google Scholar 

  14. Mandemaker, M., Bakker, M. & Stoorvogel, J. The role of governance in agricultural expansion and intensification: a global study of arable agriculture. Ecol. Soc. 16, 8 (2011).

    Article  Google Scholar 

  15. Yu, Q., Xiang, M., Wu, W. & Tang, H. Changes in global cropland area and cereal production: an inter-country comparison. Agric. Ecosyst. Environ. 269, 140–147 (2019).

    Article  Google Scholar 

  16. Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 2844 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  17. Pellegrini, P. & Fernández, R. J. Crop intensification, land use, and on-farm energy-use efficiency during the worldwide spread of the green revolution. Proc. Natl Acad. Sci. USA 115, 2335–2340 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Ceddia, M. G., Bardsley, N. O., Gomez-y-Paloma, S. & Sedlacek, S. Governance, agricultural intensification, and land sparing in tropical South America. Proc. Natl Acad. Sci. USA 111, 7242–7247 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency: recent trends and future directions. Environ. Res. Lett. 8, 044041 (2013).

    Article  ADS  Google Scholar 

  21. Wu, W. et al. Global cropping intensity gaps: increasing food production without cropland expansion. Land Use Policy 76, 515–525 (2018).

    Article  Google Scholar 

  22. Arvor, D., Dubreuil, V., Simões, M. & Bégué, A. Mapping and spatial analysis of the soybean agricultural frontier in Mato Grosso, Brazil, using remote sensing data. GeoJournal 78, 833–850 (2013).

    Article  Google Scholar 

  23. Spera, S. A. et al. Recent cropping frequency, expansion, and abandonment in Mato Grosso, Brazil had selective land characteristics. Environ. Res. Lett. 9, 064010 (2014).

    Article  ADS  Google Scholar 

  24. Spangler, K. R., Lynch, A. H. & Spera, S. A. Precipitation drivers of cropping frequency in the Brazilian Cerrado: evidence and implications for decision-making. Weather Clim. Soc. 9, 201–213 (2017).

    Article  ADS  Google Scholar 

  25. Abrahão, G. M. & Costa, M. H. Evolution of rain and photoperiod limitations on the soybean growing season in Brazil: the rise (and possible fall) of double-cropping systems. Agric. Forest Meteorol. 256–257, 32–45 (2018).

    Article  ADS  Google Scholar 

  26. Martinelli, L. A., Batistella, M., Bicudo da Silva, R. F. & Moran, E. Soy expansion and socioeconomic development in municipalities of Brazil. Land 6, 62 (2017).

    Article  Google Scholar 

  27. VanWey, L. K., Spera, S., de Sa, R., Mahr, D. & Mustard, J. F. Socioeconomic development and agricultural intensification in Mato Grosso. Phil. Trans. R. Soc. B 368, 20120155 (2013).

    Article  Google Scholar 

  28. Arvor, D., Meirelles, M., Dubreuil, V., Bégué, A. & Shimabukuro, Y. E. Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices. Appl. Geogr. 32, 702–713 (2012).

    Article  Google Scholar 

  29. Dias, L. C., Pimenta, F. M., Santos, A. B., Costa, M. H. & Ladle, R. J. Patterns of land use, extensification, and intensification of Brazilian agriculture. Glob. Change Biol. 22, 2887–2903 (2016).

    Article  ADS  Google Scholar 

  30. Barretto, A. G. O. P., Berndes, G., Sparovek, G. & Wirsenius, S. Agricultural intensification in Brazil and its effects on land-use patterns: an analysis of the 1975–2006 period. Glob. Change Biol. 19, 1804–1815 (2013).

    Article  ADS  Google Scholar 

  31. Flexor, G. & Leite, S. P. Land market and land grabbing in Brazil during the commodity boom of the 2000s. Contexto Int. 39, 393–420 (2017).

    Article  Google Scholar 

  32. Spera, S. A., Galford, G. L., Coe, M. T., Macedo, M. N. & Mustard, J. F. Land-use change affects water recycling in Brazil’s last agricultural frontier. Glob. Change Biol. 22, 3405–3413 (2016).

    Article  ADS  Google Scholar 

  33. Santos de Araújo, M. L. et al. Spatiotemporal dynamics of soybean crop in the Matopiba region, Brazil (1990–2015). Land Use Policy 80, 57–67 (2019).

    Article  Google Scholar 

  34. Lima, M., da Silva, C. A. Jr, Rausch, L., Gibbs, H. K. & Johann, J. A. Demystifying sustainable soy in Brazil. Land Use Policy 82, 349–352 (2019).

    Article  Google Scholar 

  35. Jepson, W. Private agricultural colonization on a Brazilian frontier, 1970–1980. J. Hist. Geogr. 32, 839–863 (2006).

    Article  Google Scholar 

  36. Bicudo da Silva, R. F. et al. The Sino–Brazilian telecoupled soybean system and cascading effects for the exporting country. Land 6, 53 (2017).

    Article  Google Scholar 

  37. Fuchs, R. et al. Why the US–China trade war spells disaster for the Amazon. Nature 567, 451–454 (2019).

    Article  PubMed  Google Scholar 

  38. Allen, E. & Valdes, C. Brazil’s Corn Industry and the Effect on the Seasonal Pattern of US Corn Exports (USDA Economic Research Service, 2016).

  39. Garrett, R. D., Lambin, E. F. & Naylor, R. L. The new economic geography of land use change: supply chain configurations and land use in the Brazilian Amazon. Land Use Policy 34, 265–275 (2013).

    Article  Google Scholar 

  40. Simões, D. d. C., Caixeta-Filho, J. V. & Palekar, U. S. Fertilizer distribution flows and logistic costs in Brazil: Changes and benefits arising from investments in port terminals. Int. Food Agribus. Man. Rev. 21, 407–422 (2018).

    Article  Google Scholar 

  41. Alliprandini, L. F. et al. Understanding soybean maturity groups in Brazil: environment, cultivar classification, and stability. Crop Sci. 49, 801–808 (2009).

    Article  Google Scholar 

  42. da S. Andrea, M. C., Boote, K. J., Sentelhas, P. C. & Romanelli, T. L. Variability and limitations of maize production in Brazil: potential yield, water-limited yield and yield gaps. Agric. Syst. 165, 264–273 (2018).

    Article  Google Scholar 

  43. Dias, F. S. et al. Agronomic responses of maize hybrids to row spacing and plant population in the summer and winter seasons in Brazil. Agron. J. 111, 3119–3129 (2019).

    Article  CAS  Google Scholar 

  44. de A. Salvador, M. & de Brito, J. I. B. Trend of annual temperature and frequency of extreme events in the MATOPIBA region of Brazil. Theor. Appl. Climatol. 133, 253–261 (2018).

    Article  ADS  Google Scholar 

  45. Xiong, J. et al. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J. Photogramm. Remote Sens. 126, 225–244 (2017).

    Article  ADS  Google Scholar 

  46. Waha, K. et al. Multiple cropping systems of the world and the potential for increasing cropping intensity. Glob. Environ. Change 64, 102131 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Data: Agricultural Land (% of Land Area) (World Bank Group, 2016).

  48. Pesquisas Agropecuárias (IBGE, 2018); https://biblioteca.ibge.gov.br/visualizacao/livros/liv101552.pdf

  49. de Miranda, E. E., Magalhães, L. A. & de Carvalho, C. A. Proposta de Delimitação Territorial do MATOPIBA Nota Técnica 01.2014.18 (GITE/Embrapa, 2014).

  50. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  51. Toms, J. D. & Lesperance, M. L. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).

    Article  Google Scholar 

  52. Muggeo, V. M. R. Segmented: An R package to fit regression models with broken-line relationships. R News 8, 20–25 (2008).

    Google Scholar 

  53. Sparovek, G. et al. Asymmetries of cattle and crop productivity and efficiency during Brazil’s agricultural expansion from 1975 to 2006. Elementa Sci. Anthrop. 6, 25 (2018).

    Article  Google Scholar 

  54. Ang, B. W. LMDI decomposition approach: a guide for implementation. Energy Policy 86, 233–238 (2015).

    Article  Google Scholar 

  55. Guan, D. et al. Structural decline in China’s CO2 emissions through transitions in industry and energy systems. Nat. Geosci. 11, 551–555 (2018).

    Article  ADS  CAS  Google Scholar 

  56. Zhou, F. et al. Deceleration of China’s human water use and its key drivers. Proc. Natl Acad. Sci. USA 117, 7702–7711 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Blomqvist, L., Yates, L. & Brook, B. W. Drivers of increasing global crop production: a decomposition analysis. Environ. Res. Lett. 15, 0940b6 (2020).

    Article  Google Scholar 

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Acknowledgements

This work was partially funded by the National Natural Science Foundation of China under grant nos 31701316 and 32071894, the Chinese Scholarship Council under grant no. 2017DFJ002032 and Zhejiang University.

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Authors and Affiliations

  1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China

    Jialu Xu, Henrique Vinicius de Holanda, Renhai Zhong, Hao Jiang, Yibin Ying & Tao Lin

  2. Department of Geography & Data Science Institute, University of Delaware, Newark, DE, USA

    Jing Gao

  3. Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Luis F. Rodríguez & K. C. Ting

  4. Department of Economics, Management and Sociology, University of São Paulo, Piracicaba, São Paulo, Brazil

    José Vicente Caixeta-Filho

  5. International Campus, Zhejiang University, Haining, Zhejiang, China

    Renhai Zhong & K. C. Ting

  6. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, China

    Haifeng Li

  7. School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang, China

    Zhenhong Du

  8. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Xuhui Wang

  9. Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Shaowen Wang

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  1. Jialu Xu
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Contributions

J.X. and T.L. conceived and designed the experiments; J.X., H.V.d.H. and T.L. performed the experiments; J.X., J.G., H.V.d.H., R.Z., H.J. and T.L. analysed the data; J.X., J.G., L.F.R., J.V.C.-F., H.L., Z.D., X.W., S.W., K.C.T., Y.Y. and T.L. contributed materials/analysis tools; J.X., J.G., H.V.d.H., L.F.R., J.V.C.-F., H.L., Z.D., X.W., S.W., K.C.T., Y.Y. and T.L. wrote the paper.

Corresponding author

Correspondence to Tao Lin.

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The authors declare no competing interests.

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Peer review information Nature Food thanks Michaela Theurl, Michael Obersteiner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs 1–6 and Tables 1 and 2.

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Xu, J., Gao, J., de Holanda, H.V. et al. Double cropping and cropland expansion boost grain production in Brazil. Nat Food 2, 264–273 (2021). https://doi.org/10.1038/s43016-021-00255-3

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