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The impact of climate change on the productivity of conservation agriculture

Nature Climate Change volume 11, pages 628–633 (2021)Cite this article

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Abstract

Conservation agriculture (CA) is being promoted as a set of management practices that can sustain crop production while providing positive environmental benefits. However, its impact on crop productivity is hotly debated, and how this productivity will be affected by climate change remains uncertain. Here we compare the productivity of CA systems and their variants on the basis of no tillage versus conventional tillage systems for eight major crop species under current and future climate conditions using a probabilistic machine-learning approach at the global scale. We reveal large differences in the probability of yield gains with CA across crop types, agricultural management practices, climate zones and geographical regions. For most crops, CA performed better in continental, dry and temperate regions than in tropical ones. Under future climate conditions, the performance of CA is expected to mostly increase for maize over its tropical areas, improving the competitiveness of CA for this staple crop.

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Fig. 1: Accumulated fractions of cropping area achieving a given probability of yield gain with CA (+F+WD) versus CT–R–SC (+F+WD).
Fig. 2: Accumulated fractions of the cropping area for different levels of change in the probability of yield gain with CA (+F+WD) versus CT–R–SC (+F+WD).
Fig. 3: Probability of yield gain with CA and their variants versus CT for maize under current climate conditions.
Fig. 4: Probability of yield gain with CA systems and their variants versus CT for maize under future climate conditions.
Fig. 5: The change of probability of yield gain with CA and their variants versus CT for maize under climate change (future versus current).

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Data availability

The dataset10 is made fully available and described in a data paper11. The dataset can be accessed using the following link: https://doi.org/10.6084/m9.figshare.12155553.

Code availability

All the R and MATLAB codes are available in figshare and can be accessed through the link https://doi.org/10.6084/m9.figshare.14427977 or on request from the corresponding author.

References

  1. Bisht, J. K, Meena, V. S, Mishra, P. K. & Pattanayak, A. (eds) Conservation Agriculture: An Approach to Combat Climate Change in Indian Himalaya (Springer, 2016).

  2. Farooq, M. & Siddique, K. H. M. (eds) Conservation Agriculture (Springer, 2015).

  3. Giller, K. E. et al. Beyond conservation agriculture. Front. Plant Sci. 6, 870 (2015).

  4. Jat, S. L. et al. Differential response from nitrogen sources with and without residue management under conservation agriculture on crop yields, water-use and economics in maize-based rotations. Field Crops Res. 236, 96–110 (2019).

    Article  Google Scholar 

  5. Steward, P. R. et al. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric. Ecosyst. Environ. 251, 194–202 (2018).

    Article  Google Scholar 

  6. Pittelkow, C. M. et al. When does no-till yield more? A global meta-analysis. Field Crops Res. 183, 156–168 (2015).

    Article  Google Scholar 

  7. Conservation Agriculture (FAO, 2020); http://www.fao.org/conservation-agriculture/en/

  8. Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).

    Article  CAS  Google Scholar 

  9. Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: a review. Field Crops Res. 183, 56–68 (2015).

    Article  Google Scholar 

  10. Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage practice. figshare https://doi.org/10.6084/m9.figshare.12155553 (2020).

  11. Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. Sci. Data 8, 33 (2020).

    Article  Google Scholar 

  12. University of Delaware Air Temperature & Precipitation (NOAA/OAR/ESRL PSL, 2020); https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html

  13. CPC Global Daily Temperature (NOAA/OAR/ESRL PSL, 2020); https://www.esrl.noaa.gov/psd/data/gridded/data.cpc.globaltemp.html

  14. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).

    Article  Google Scholar 

  15. Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).

    Article  Google Scholar 

  16. Crop Calendar Dataset: netCDF 5 degree (Univ. of Wisconsin-Madison, 2020); https://nelson.wisc.edu/sage/data-and-models/crop-calendar-dataset/netcdf0-5degree.php

  17. Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition 278–282 (IEEE, 1995).

  18. Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).

    Article  Google Scholar 

  19. Laborde, J. P., Wortmann, C. S., Blanco-Canqui, H., Baigorria, G. A. & Lindquist, J. L. Identifying the drivers and predicting the outcome of conservation agriculture globally. Agric. Syst. 177, 102692 (2020).

    Article  Google Scholar 

  20. Su, Z. et al. Effects of conservation tillage practices on winter wheat water-use efficiency and crop yield on the Loess Plateau, China. Agric. Water Manag. 87, 307–314 (2007).

    Article  Google Scholar 

  21. van Wie, J. B., Adam, J. C. & Ullman, J. L. Conservation tillage in dryland agriculture impacts watershed hydrology. J. Hydrol. 483, 26–38 (2013).

    Article  Google Scholar 

  22. Shukla, M. K., Lal, R. & Ebinger, M. Tillage effects on physical and hydrological properties of a typica argiaquoll in Central Ohio. Soil Sci. 168, 802–811 (2003).

    Article  CAS  Google Scholar 

  23. Shen, Y., McLaughlin, N., Zhang, X., Xu, M. & Liang, A. Effect of tillage and crop residue on soil temperature following planting for a Black soil in Northeast China. Sci. Rep. 8, 4500 (2018).

    Article  Google Scholar 

  24. Zuazo, V. H. D. & Pleguezuelo, C. R. R. Soil-erosion and runoff prevention by plant covers. A review. Agron. Sustain. Dev. 28, 65–86 (2008).

    Article  Google Scholar 

  25. Shaxson, F. & Barber, R. Optimizing Soil Moisture for Plant Production: The Significance of Soil Porosity (FAO, 2003).

  26. Derpsch, R., Sidiras, N. & Roth, C. H. Results from studies made from 1977–1984 to control erosion by no-tillage techniques in Paraná, Brazil. Soil Tillage Res. 8, 253–263 (1986).

    Article  Google Scholar 

  27. TerAvest, D., Carpenter-Boggs, L., Thierfelder, C. & Reganold, J. P. Crop production and soil water management in conservation agriculture, no-till, and conventional tillage systems in Malawi. Agric. Ecosyst. Environ. 212, 285–296 (2015).

    Article  Google Scholar 

  28. Page, K. L., Dang, Y. P. & Dalal, R. C. The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sust. Food Syst. 4, 31 (2020).

  29. Halvorson, A. D. et al. Spring wheat response to tillage and nitrogen fertilization in rotation with sunflower and winter wheat. Agron. J. 92, 136–144 (2000).

    Article  Google Scholar 

  30. Radford, B. J. & Thornton, C. M. Effects of 27 years of reduced tillage practices on soil properties and crop performance in the semi-arid subtropics of Australia. Int. J. Energy Environ. Econ. 19, 565 (2011).

    Google Scholar 

  31. García-Palacios, P., Alarcón, M. R., Tenorio, J. L. & Moreno, S. S. Ecological intensification of agriculture in drylands. J. Arid. Environ. 167, 101–105 (2019).

    Article  Google Scholar 

  32. Scopel, E. et al. Conservation agriculture cropping systems in temperate and tropical conditions, performances and impacts. A review. Agron. Sustain. Dev. 33, 113–130 (2013).

    Article  Google Scholar 

  33. Thierfelder, C. & Wall, P. C. Investigating conservation agriculture (CA) systems in Zambia and Zimbabwe to mitigate future effects of climate change. J. Crop Improve. 24, 113–121 (2010).

    Article  Google Scholar 

  34. Lal, R. The role of residues management in sustainable agricultural systems. J. Sustain. Agric. 5, 51–78 (1995).

    Article  Google Scholar 

  35. Thierfelder, C. et al. Complementary practices supporting conservation agriculture in southern Africa. A review. Agron. Sustain. Dev. 38, 16 (2018).

    Article  Google Scholar 

  36. Corbeels, M., Naudin, K., Whitbread, A. M., Kühne, R. & Letourmy, P. Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa. Nat. Food 1, 447–454 (2020).

    Article  Google Scholar 

  37. Smith, P., Powlson, D. S., Glendining, M. J. & Smith, J. U. Preliminary estimates of the potential for carbon mitigation in European soils through no‐till farming. Glob. Change Biol. 4, 679–685 (1998).

    Article  Google Scholar 

  38. Williams, A. et al. A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. Sci. Rep. 8, 8467 (2018).

    Article  Google Scholar 

  39. Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of conservation agriculture: justification, sustainability and uptake. Int. J. Agric. Sustain. 7, 292–320 (2009).

    Article  Google Scholar 

  40. Hobbs, P. R., Sayre, K. & Gupta, R. The role of conservation agriculture in sustainable agriculture. Phil. Trans. R. Soc. B 363, 543–555 (2008).

    Article  Google Scholar 

  41. Govaerts, B. et al. Conservation agriculture as a sustainable option for the central Mexican highlands. Soil Tillage Res. 103, 222–230 (2009).

    Article  Google Scholar 

  42. Su, Y., Gabrielle, B., Beillouin, D. & Makowski, D. High probability of yield gain through conservation agriculture in dry regions for major staple crops. Sci. Rep. 11, 3344 (2021).

    Article  CAS  Google Scholar 

  43. Soil Texture Map (Univ. of Tokyo, 2020); http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html

  44. Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  45. Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).

    Article  Google Scholar 

  46. ESGF Project (Lawrence Livermore National Laboratory, 2020); https://esgf-node.llnl.gov/projects/esgf-llnl/

  47. 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. Global Biogeochem. Cycles 24, GB1011 (2010).

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Acknowledgements

This work was supported by the ANR under the ‘Investissements d’avenir’ programme with the reference ANR-16-CONV-0003 (CLAND) and by the INRAE CIRAD meta-program ‘GloFoods’.

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Authors and Affiliations

  1. UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, Thiverval-Grignon, France

    Yang Su & Benoit Gabrielle

  2. UMR Applied Mathematics and Computer Science (MIA518), INRAE AgroParisTech, Université Paris-Saclay, Paris, France

    David Makowski

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Contributions

Y.S. wrote the main manuscript text and B.G. and D.M. modified it. Y.S., B.G. and D.M. worked together to prepare the figures and tables. Y.S. collected the data. Y.S., B.G. and D.M. designed and built the models to process the data. Y.S. worked on the model cross-validation. All authors reviewed the manuscripts.

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Correspondence to Yang Su.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Marc Corbeels, Krishna Naudin, Christian Thierfelder 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.

Extended data

Extended Data Fig. 1 Accumulated fraction of the cropping area as a function of the probability of yield gain under CA (+F+WD -Irrigation) versus CT–R–SC (+F+WD -Irrigation) systems in different climate regions.

Accumulated fraction of the cropping area as a function of the probability of yield gain under CA (+F+WD – Irrigation) versus CT–R–SC with fertilization (+F) and weed and pest control (+WD) without irrigation (–Irrigation) for eight major crops (a–h) and different climate zones. The results are based on the average climate conditions over 2021–2020 simulated by the Ipsl-cm5a-lr climate model and RCP 4.5 scenario.

Extended Data Fig. 2 Relative importance ranking of the model inputs.

The importance was defined by the mean decrease in accuracy in the ‘cforest’ model. Where ‘PB’ indicates precipitation balance over crop growing season; ‘Tmax’ indicates maximum air temperature over crop growing season;’Tave’ indicates average air temperature over crop growing season; ‘Tmin’ indicates minimum air temperature over crop growing season; ‘Crop’ indicates the crop species;’ST’ indicates soil texture; ‘SCNT’ indicates soil cover management under the variants of no tillage systems; ‘SCCT’ indicates soil cover management under CT systems; ‘RNT’ indicates crop rotation management under the variants of no tillage systems; ‘RCT’ indicates crop rotation management under CT systems; ‘FNT’ indicates management of crop fertilization under the variants of no tillage systems; ‘FCT’ indicates crop management of crop fertilization under CT systems; ‘WDNT’ indicates management of weed and pest control under the variants of no tillage systems; ‘WDCT’ indicates crop management of weed and pest control under CT systems.

Extended Data Fig. 3 The accumulated fraction of the cropping area in different level of change on the probability of yield gain under CA (+F+WD) versus CT–R–SC (+F+WD) under different crops, climate models and RCP scenarios.

The accumulated fraction of the cropping area in different level of change on the probability of yield gain under CA (+F+WD) versus CT–R–SC (+F+WD) for different crops, climate models and RCP scenarios. The results are based on the average climate data in different RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) in Ipsl-cm5a-lr model, and RCP 4.5 scenario in different climate models (Ipsl-cm5a-lr, Gfdl-esm2m, Hadgem2-es, Miroc5) for both current (2021–2020) and future (2051–2060) scenarios.

Extended Data Fig. 4 Distributions of experiment site for each crop.

This map and the corresponding dataset are presented in ref. 11, 42. This figure was generated by MATLAB R2020a (Version 9.8.0.1451342, https://fr.mathworks.com/products/matlab.html). In this meta-dataset (ref. 11), 4403 paired yield observations were extracted from NT and CT for 8 major crop species, including 370 observations for barley (232 observations for spring barley and 138 for winter barley), 94 observations for cotton, 1690 observations for maize, 195 observation for rice, 160 observations for sorghum, 583 observations for soybean, 61 observations for sunflower, 1250 observations for wheat (1041 observations for winter wheat and 209 observations for spring wheat) in 50 countries from 1980 to 2017.

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Supplementary Table 1, Figs. 1–8 and references.

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Su, Y., Gabrielle, B. & Makowski, D. The impact of climate change on the productivity of conservation agriculture. Nat. Clim. Chang. 11, 628–633 (2021). https://doi.org/10.1038/s41558-021-01075-w

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