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Crop mixtures outperform rotations and landscape mosaics in regulation of two fungal wheat pathogens: a simulation study

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Abstract

Context

Crop rotations, within-field mixtures, and landscape mosaics including susceptible and resistant crops are three commonly adopted crop diversification strategies that can limit crop epidemics. Typically, the effects of crop diversification at these three scales have been studied separately, on single pathogen species, and with low environmental variability.

Objectives

We aim to compare the disease-limitation effect of these three types of crop diversification on two highly damaging fungal pathogens of wheat Puccinia recondita (WLR) and Zymoseptoria tritici (STB) and under varying weather conditions (warmer or cooler climate for WLR, wetter or drier conditions for STB).

Methods

We built a dynamic mathematical model of epidemics at the field scale (based on classical Susceptible-Exposed-Infectious-Removed epidemiological models) embedded in a spatially explicit landscape grid framework. We use it to simulate an agricultural landscape in which diversification translates into different proportions of wheat and resistant crops in the landscape.

Results

In our simulations, for both pathogens and in all weather conditions, within-field crop mixtures had the greatest impact in limiting epidemics, crop rotations were second-best, while landscape mosaics were the least effective. We also found that the threshold above which further addition of resistant plants to crop mixtures would not cause further disease limitation to be dependent on weather conditions. The more favorable the weather is for pathogens the more resistant plants are required.

Conclusions

Our findings imply that interactions between spatial scale of crop diversification, pathogen characteristics and weather conditions should be considered in order to maximize benefits from disease-regulation properties of diversified cropping systems under climate change.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. The Matlab implementation of the model is available upon request by e-mail to the corresponding author: pierre-antoine.precigout@inrae.fr.

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Acknowledgements

This work was funded by the French National Research Agency under the Programme “Investissements d’Avenir” under the reference ANR 17 MPGA 0004 and by the National Research Institute for Agriculture, Food and the Environment (INRAE). We thank Doyle McKey and Olivier Dangles for helpful comments during the preparation of the manuscript and English proofreading.

Funding

This work was funded by the French National Research Agency under the Programme “Investissements d’Avenir” under the reference ANR 17 MPGA 0004 and by the National Research Institute for Agriculture, Food and the Environment (INRAE).

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Authors and Affiliations

  1. UMR EcoSys, INRAE - AgroParisTech, Route de la Ferme, 78850, Thiverval-Grignon, France

    P.-A. Précigout & C. Robert

  2. CEFE, University Montpellier, CNRS, EPHE, IRD, UnivPaul Valéry Montpellier 3, Montpellier, France

    D. Renard

  3. CERES: Centre d’Enseignement et de foRmation sur l’Environnement et la Société, École Normale supérieure, 24 Rue Lhomond, 75230, Paris, France

    J. Sanner & D. Claessen

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Contributions

Conceptualization: DC & CR. Model Development: DC, JS & CR. Simulation schedule: DC, JS, P-AP & CR. Simulation Analysis: P-AP, DR, DC & CR. Writing, Review and Editing: P-A.P., D.R. & C.R.

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10980_2022_1545_MOESM1_ESM.tif

Supplementary file1 (TIF 1865 kb) Supplementary Fig. 1 Seasonal dynamics of the green Leaf Area Index (LAI) of wheat (solid black line, the susceptible and resistant cultivars share the same LAI) and pea (dotted green line). Time is expressed in degree-days (dd)

10980_2022_1545_MOESM2_ESM.tif

Supplementary file2 (TIF 4464 kb) Supplementary Fig. 2 Coherence tests of the effect of rainfall patterns on STB epidemics. A: seasonal dynamics of crop growth (gLAI: green Leaf Area Index when no epidemics occur; S: surface of susceptible crop remaining healthy) and epidemiological dynamics (E: latent crop leaf surface; I: infectious crop leaf surface) for six successive cropping seasons. B: Total of crop leaf surface infected by STB. Below the figure, we represented the rain comb patterns we used to simulate rainfall in the model, corresponding to transformed data from the Grignon research station recorded between (here) 1994 and 2000. Year 1994–95 was a favourable year for STB, while year 1996–97 was unfavourable. This sequence os part of the 1993–2006 sequence used as average weather condition

10980_2022_1545_MOESM3_ESM.tif

Supplementary file3 (TIF 2764 kb) Supplementary Fig. 3 Coherence tests regarding the starting date of WLR epidemics. Seasonal dynamics of crop growth (gLAI: green Leaf Area Index when no epidemics occur; S: surface of susceptible crop remaining healthy) and epidemiological dynamics (E: latent crop leaf surface; I: infectious crop leaf surface) for A: epidemics starting early (favorable weather conditions for the pathogen); B: average starting date of epidemics (average weather conditions for the pathogen) and C: late starting date of epidemics (unfavorable weather conditions for the pathogen)

10980_2022_1545_MOESM4_ESM.tif

Supplementary file4 (TIF 4654 kb) Supplementary Fig. 4 Combined effect of diversification strategies and within-season spore mortality rate on the intensity of epidemics (AUDPC) of WLR and STB. Here the resistant crop is a partially resistant wheat cultivar. Reference overwintering corresponds to \(\rho\) = 0.01 for WLR and \(\rho\) = 0.002 for STB. High within-season spore mortality rate corresponds to \(\rho\) = 0.05 for WLR and \(\rho\) = 0.008 for STB. Low within-season spore mortality rate corresponds to \(\rho\) = 0.005 for WLR and \(\rho\) = 0.0005 for STB

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Précigout, PA., Renard, D., Sanner, J. et al. Crop mixtures outperform rotations and landscape mosaics in regulation of two fungal wheat pathogens: a simulation study. Landsc Ecol 38, 77–97 (2023). https://doi.org/10.1007/s10980-022-01545-2

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