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The impact of climate change on the productivity of conservation agriculture

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).

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.

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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’.

Author information

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Authors

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.

Corresponding author

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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