{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T02:26:28Z","timestamp":1778984788686,"version":"3.51.4"},"reference-count":45,"publisher":"The Royal Society","issue":"201","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/royalsociety.org\/journals\/ethics-policies\/data-sharing-mining\/"}],"content-domain":{"domain":["royalsocietypublishing.org"],"crossmark-restriction":true},"short-container-title":["J. R. Soc. Interface."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p>\n            Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete\u2014i.e. resistant strains are unaffected by the chemical and resistance requires only a single genetic change\u2014using the lowest possible dose ensuring sufficient control is well known as the optimal resistance management strategy. However, partial resistance (where resistant strains are still partially suppressed by the fungicide) and quantitative resistance (where a range of resistant strains are present) remain ill-understood. Here, we use a model of quantitative fungicide resistance (parametrized for the economically important fungal pathogen\n            <jats:italic>Zymoseptoria tritici<\/jats:italic>\n            ) which handles qualitative partial resistance as a special case. Although low doses are optimal for resistance management, we show that for some model parametrizations the resistance management benefit does not outweigh the improvement in control from increasing doses. This holds for both qualitative partial resistance and quantitative resistance. Via a machine learning approach (a gradient-boosted trees model combined with Shapley values to facilitate interpretability), we interpret the effect of parameters controlling pathogen mutation and characterising the fungicide, in addition to the time scale of interest.\n          <\/jats:p>","DOI":"10.1098\/rsif.2022.0685","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T07:05:31Z","timestamp":1681887931000},"update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative"],"prefix":"10.1098","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6560-6829","authenticated-orcid":true,"given":"Nick P.","family":"Taylor","sequence":"first","affiliation":[{"name":"Department of Plant Sciences, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-8672","authenticated-orcid":true,"given":"Nik J.","family":"Cunniffe","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, University of Cambridge, Cambridge, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"175","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2022239118"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.phyto.43.113004.133839"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41559-018-0793-y"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-phyto-080516-035357"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43016-019-0010-4"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrmicro.2017.76"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43016-020-0075-0"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/bs.af2s.2017.09.007"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2016.0026"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1111\/ppa.13467"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2014.06.002"},{"key":"e_1_3_5_13_2","first-page":"1","article-title":"Using epidemiological principles to explain fungicide resistance management strategies: why do mixtures outperform alternations?","volume":"44","author":"Elderfield JAD","year":"2018","unstructured":"Elderfield JAD, Lopez-Ruiz FJ, Cunniffe NJ. 2018 Using epidemiological principles to explain fungicide resistance management strategies: why do mixtures outperform alternations? Phytopathology 44, 1-69.","journal-title":"Phytopathology"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-06-12-0142-R"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-10-10-0290"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-3059.2010.02380.x"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-07-13-0204-R"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-08-16-0297-R"},{"key":"e_1_3_5_19_2","first-page":"55","article-title":"Optimal resistance management for mixtures of high-risk fungicides: robustness to the initial frequency of resistance and pathogen sexual reproduction","volume":"133","author":"Taylor N","year":"2022","unstructured":"Taylor N, Cunniffe NJ. 2022 Optimal resistance management for mixtures of high-risk fungicides: robustness to the initial frequency of resistance and pathogen sexual reproduction. Phytopathology 133, 55-69.","journal-title":"Phytopathology"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1094\/PHYTO-03-13-0061-R"},{"key":"e_1_3_5_21_2","unstructured":"Kirikyali N Diez P Luo J Hawkins N Fraaije B. 2017 Azole and SDHI sensitivity of Zymoseptoria tritici field populations sampled in France Germany and United Kingdom during 2015. In Modern fungicides and antifungal compounds (eds HB Deising B Fraaije A Mehl EC Oerke H Sierotzki G Stammler) vol. VIII pp. 153\u2013158. 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