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Vapour pressure deficit determines critical thresholds for global coffee production under climate change

Abstract

Our understanding of the impact of climate change on global coffee production is largely based on studies focusing on temperature and precipitation, but other climate indicators could trigger critical threshold changes in productivity. Here, using generalized additive models and threshold regression, we investigate temperature, precipitation, soil moisture and vapour pressure deficit (VPD) effects on global Arabica coffee productivity. We show that VPD during fruit development is a key indicator of global coffee productivity, with yield declining rapidly above 0.82 kPa. The risk of exceeding this threshold rises sharply for most countries we assess, if global warming exceeds 2 °C. At 2.9 °C, countries making up 90% of global supply are more likely than not to exceed the VPD threshold. The inclusion of VPD and the identification of thresholds appear critical for understanding climate change impacts on coffee and for the design of adaptation strategies.

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Fig. 1: Marginal effects of the key climate drivers of Arabica yields when the effects of all other covariates are held constant from the best GAM model identified from model selection.
Fig. 2: Predicted coffee yield response to VPD and the estimated VPD threshold.
Fig. 3: Predicted coffee yield response to VPD in a model including soil moisture.
Fig. 4: Probability of surpassing the VPD threshold in Arabica (C. arabica)-producing countries under different climate scenarios.
Fig. 5: The relationship between global warming, the VPD threshold and global Arabica coffee supply.

Data availability

The analysis is based on publicly available datasets. TerraClimate data are from http://www.climatologylab.org/terraclimate.html. Coffee yield data are from http://www.fao.org/faostat/en/#home. Coffee mapping data are from https://www.mapspam.info/.

Code availability

Code to replicate the key threshold analysis results of the study is at https://doi.org/10.7910/DVN/QETV5H. Correspondence and requests for additional materials should be addressed to J. Kath (jarrod.kath@usq.edu.au).

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Acknowledgements

We thank J. Moat (RBG, Kew), K. Hewison and staff from Centre of Applied Climate Sciences, USQ for discussions about this paper. We thank several anonymous reviewers whose feedback helped improve the manuscript. This study was supported by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety—International Climate Initiative (IKI) and also implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements (https://ccafs.cgiar.org/donors).

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Authors

Contributions

J.K. conceived the initial study based on conversations with A.C., P.V.A., A.P.D., Y.F., V.B. and S.P.; Y.F. and J.K. performed the threshold analysis; and J.K., R.K. and T.N.-H. carried out supporting analyses. J.K. and S.P. wrote the manuscript. J.K., T.N.-H. and T.M. linked and analysed the climate data. T.M., S.M. and R.S contributed to the writing of the manuscript. All authors contributed to the critical review and writing of the manuscript.

Corresponding author

Correspondence to Jarrod Kath.

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The authors declare no competing interests.

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Nature Food thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The influence of each predictor’s main effect in the best model.

The solid black line is the mean effect and the grey shaded areas are the 95% confidence intervals and black dots are residuals. The y-axis is the value of the centred smooth and represents the contribution made to the fitted value of that smooth function.

Extended Data Fig. 2 Predicted coffee yield response to mean maximum temperature and the estimated mean maximum temperature threshold.

Arabica (C. arabica) yields relationship with mean maximum temperature in the growing season while other covariates are held constant at their mean. Black dashed line is the estimated mean maximum temperature threshold. The blue line is the relationship between mean maximum temperature and yield before the 29.22 °C threshold and the dashed red line after passing the mean maximum temperature threshold. The inset box shows predicted coffee yields response across the entire mean maximum temperature gradient. Grey coloured shaded areas are 95% confidence intervals.

Extended Data Fig. 3 Results of analyses testing whether threshold values vary when a particular country or block of time is excluded from analysis.

The (-) designates the country (or time period) that has been excluded from analysis and the value return is the median threshold value. The black dashed line is the median threshold value of all analyses and the dashed red lines represent the 95% confidence interval of all analyses. The grey histograms and shaded in different colours for each individual hold-out analyses. Panel a. is threshold of the country-wise hold-out analysis for growing season vapour pressure deficit. Panel b., the block of years hold-out analysis for growing season vapour pressure deficit. Panel c. is threshold of the country-wise hold-out analysis for growing season mean maximum temperature. Panel d., the block of years hold-out analysis for growing season mean maximum temperature.

Extended Data Fig. 4 The relationship between soil moisture and vapour pressure deficit and projected changes in soil moisture under climate change.

Panel a., Scatterplot showing the correlation between growing season soil moisture and vapour pressure deficit. Panel b., Growing season soil moisture under baseline (1985–2015) conditions. Additional boxplots show global warming scenarios of 2 °C (mustard) and 4 °C (red). For each scenario N = 31 (31 years). The centre line of boxplots is the median, lower and upper sections are 25th and 75th percentiles, respectively, whiskers show the full range of the data, except for outliers which are shown as points.

Extended Data Fig. 5 Marginal effects of VPD on Arabica yields under different soil moisture scenarios.

Panel a., all data (n = 648). Panel b., low soil moisture (that is below the median total growing season soil moisture of 851 mm, n = 323). Panel c., high soil moisture (that is above the median total growing season soil moisture of 851 mm, n = 325). Points are residuals. Note the lack of data at low VPD in Panel c., for the high soil moisture scenario.

Extended Data Fig. 6 The density distribution of growing season VPD for the top four Arabica (C. arabica) producing countries.

Selection of top four production countries is based on 2019 production levels https://fdc.nal.usda.gov/. Blue shaded density plots are baseline conditions (1985–2015), yellow density plots represent a 2 °C warming scenario and red density plots a 4 °C warming scenario. Dark shaded areas on density plots represent the range of the data from TerraClimate climate change scenarios and extended light areas are extrapolations. Dashed vertical lines represent the 0.82 kPa VPD threshold. Calculations of the probability of exceeding VPD thresholds were made on the range of actual climate change scenario data (that is, the darker shaded areas of the density plots).

Extended Data Fig. 7 Density plots showing the distribution of median vapour pressure deficit (VPD) for Brazil at mean annual global temperatures corresponding with a probability of 0.25, 0.5, 0.75 and 1 of exceeding the 0.82 kPa VPD threshold.

Dark shaded areas on density plots represent the range of the data from TerraClimate climate change scenarios and extended light areas are extrapolations. Calculations of the probability of exceeding VPD thresholds were made on the range of actual climate change scenario data (that is, the darker shaded areas of the density plots).

Extended Data Table 1 The best models from multi-model selection

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Supplementary Figs. 1–3 and Tables 1 and 2.

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Kath, J., Craparo, A., Fong, Y. et al. Vapour pressure deficit determines critical thresholds for global coffee production under climate change. Nat Food 3, 871–880 (2022). https://doi.org/10.1038/s43016-022-00614-8

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