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Protecting the Amazon forest and reducing global warming via agricultural intensification

Nature Sustainability volume 5, pages 1018–1026 (2022)Cite this article

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Abstract

The Amazon basin includes 550 Mha covered with rainforests, and 60% of this area is in Brazil. The conversion of rainforest for soybean production raises concerns about how Brazil can reconcile production and environmental goals. Here we investigated the degree to which intensification could help Brazil produce more soybean without further encroachment on the Amazon forest. Our analysis shows that the continuation of current trends in soybean yield and area would lead to the conversion of an additional 5.7 Mha of forests and savannahs during the next 15 years, with an associated 1,955 Mt of CO2e released into the atmosphere. In contrast, the acceleration of yield improvement, coupled with the expansion of soybean area only in areas currently used for livestock production, would allow Brazil to produce 162 Mt of soybean without deforestation and with 58% lower global climate warming relative to that derived from the continuation of current trends.

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Fig. 1: Trends in soybean area and yield in main producing areas in Brazil.
Fig. 2: Attainable yield and yield gaps across soybean-producing areas in Brazil.
Fig. 3: Changes in soybean production with different yield and land use change scenarios in Brazil.
Fig. 4: Land conversion, GWP and gross income associated with different scenarios of intensification and land use change in soybean-based systems in Brazil.

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Data availability

The data on yield potential and yield gaps that support the findings of this study are publicly available via the Global Yield Gap Atlas website (www.yieldgap.org). The data that support the findings of this study are also available from the corresponding author upon request. Source data are provided with this paper.

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Acknowledgements

This project was funded by the International Plant Nutrition Institute (grant no. INS-19/0007 to P.G.), the Research Foundation of the State of São Paulo (FAPESP 433 grant nos 2017/20925-0, 2018/06396-7 and 2021/00720-0 to F.R.M.), the Brazilian Research Council (CNPq grant nos 130972/2019-3, 425174/2018-2 and 300916/2018-3 to F.R.M.), the Research Foundation of the State of Rio Grande do Sul (FAPERGS grant no. 17/2551-0000775-1 to A.J.Z.) and the Global Engagement Office at the Institute of Agriculture and Natural Resources at the University of Nebraska–Lincoln (UNL) through the FAPESP–UNL SPRINT Program (grant no. 2017/50445-0 to P.G.). F.R.M. received financial support from the Fulbright programme to support a six-month stay at UNL.

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Author notes

  1. These authors contributed equally: Fabio R. Marin, Alencar J. Zanon.

Authors and Affiliations

  1. Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo, Piracicaba, Brazil

    Fabio R. Marin, Evandro H. F. M. Silva & Luis A. S. Antolin

  2. Universidade Federal de Santa Maria, Santa Maria, Brazil

    Alencar J. Zanon, Gean L. Richter, Bruna S. M. R. Ribeiro & Giovana G. Ribas

  3. Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, USA

    Juan P. Monzon, José F. Andrade & Patricio Grassini

  4. National Scientific and Technical Research Council (CONICET), Balcarce, Argentina

    Juan P. Monzon

  5. Department of Plant Production, University of Buenos Aires, IFEVA-CONICET, Buenos Aires, Argentina

    José F. Andrade

  6. College of Agronomy, Federal University of Goias, Goiania, Brazil

    Rafael Battisti

  7. EMBRAPA Arroz e Feijão, Santo Antônio de Goiás, Brazil

    Alexandre B. Heinemann

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  1. Fabio R. Marin
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Contributions

F.R.M., P.G. and A.J.Z. conceived the project. E.H.F.M.S., G.L.R., L.A.S.A., B.S.M.R.R. and G.G.R. collected the data and ran the model simulations with input from J.P.M., P.G., A.B.H. and R.B. P.G., J.P.M. and J.F.A. analysed the data. P.G., J.F.A., J.P.M., F.R.M. and A.J.Z. wrote the manuscript with input from all authors.

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Correspondence to Patricio Grassini.

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Nature Sustainability thanks Wan Yee Lam, Stoécio Maia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Historical trends of yield and harvested area for soybean and second-crop maize in Brazil.

Fitted models are shown in (a) and (b), but not in (c) to avoid overlapping. Slopes of the fitted linear regression models are shown. Slopes were statistically different from zero in all cases as evaluated using double-tailed, t-tests: P < 0.0001 for all slopes in (a) and (b) and P = 0.0075 (Amazon), P = 0.0113 (Cerrado), and P = 0.0057 (A. Forest) for slopes in panel (c). Data on yield and crop area was retrieved from IGBE16.

Source data

Extended Data Fig. 2 Land use change driven by soybean production in Brazil.

Proportion of land type by year 2000 that was converted for soybean production during the 2008-2019 period as estimated from the MAPBIOMAS Project – Collection 5.010. Separate pie charts are shown for the whole Brazil and for each of the soybean producing regions. See Supplementary Information for details on calculations of land-use change.

Extended Data Fig. 3 Evaluation of crop models used for estimation of yield potential for maize and soybean in Brazil.

Comparison of simulated and observed phenology (left) and grain yields (right) for soybean (upper panels) and maize (bottom panel) based on well-managed experiments conducted across main producing regions in Brazil, where crops were grown without nutrient limitations and kept free from incidence of biotic stresses such as weeds, insect pests, and pathogens. Phenological stages for soybean and maize based on Fehr and Caviness65 and Ritchie et al.66, respectively, are shown. In the case of soybean, stages are emergence (VE), unifoliate leaves (V1), first open flower (R2), beginning pod setting (R3), beginning seed filling (R5), physiological maturity (R7) and harvest maturity (R8). In the case of maize, stages are silking (R1) and physiological maturity (R6). In all cases, stages are reported based on their date of occurrence after sowing (DAS). The solid red line represents y = x and the dashed red lines represent ± 20% deviation from the y = x line. The latter is considered a good threshold to assess accuracy in model prediction. The root mean square error is shown in absolute terms (RMSE).

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Supplementary Information including details on the methods and underlying databases.

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Marin, F.R., Zanon, A.J., Monzon, J.P. et al. Protecting the Amazon forest and reducing global warming via agricultural intensification. Nat Sustain 5, 1018–1026 (2022). https://doi.org/10.1038/s41893-022-00968-8

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