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      Bt cotton area contraction drives regional pest resurgence, crop loss, and pesticide use

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          Summary

          Genetically‐modified crops expressing Bacillus thuringiensis (Bt) proteins have been widely cultivated, permitting an effective non‐chemical control of major agricultural pests. While their establishment can enable an area‐wide suppression of polyphagous herbivores, no information is available on the impact of Bt crop abandonment in entire landscape matrices. Here, we detail a resurgence of the cosmopolitan bollworm Helicoverpa armigera following a contraction of Bt cotton area in dynamic agro‐landscapes over 2007–2019 in North China Plain. An 80% reduction in Bt cotton was mirrored in a 1.9‐fold increase of ambient H. armigera population levels, culminating in 1.5–2.1‐fold higher yield loss and a 2.0–4.4‐fold increase in pesticide use frequency in non‐Bt crops (i.e. maize, peanut, soybean). Our work unveils the fate of herbivorous insect populations following a progressive dis‐use of insecticidal crop cultivars, and hints at how tactically deployed Bt crops could be paired with agro‐ecological measures to mitigate the environmental footprint of crop production.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              A general and simple method for obtainingR2from generalized linear mixed-effects models

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                Author and article information

                Contributors
                luyanhui@caas.cn
                Journal
                Plant Biotechnol J
                Plant Biotechnol J
                10.1111/(ISSN)1467-7652
                PBI
                Plant Biotechnology Journal
                John Wiley and Sons Inc. (Hoboken )
                1467-7644
                1467-7652
                18 October 2021
                February 2022
                : 20
                : 2 ( doiID: 10.1111/pbi.v20.2 )
                : 390-398
                Affiliations
                [ 1 ] State Key Laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant Protection Chinese Academy of Agricultural Sciences Beijing China
                [ 2 ] National Agro‐Technical Extension and Service Center Beijing China
                [ 3 ] Université Côte d’Azur INRAE CNRS UMR ISA Nice France
                [ 4 ] Environment and Production Technology Division International Food Policy Research Institute Washington DC USA
                Author notes
                [*] [* ] * Correspondence (Tel: 861062815924; fax: 861062896114; email: luyanhui@ 123456caas.cn )

                Author information
                https://orcid.org/0000-0002-6552-0248
                Article
                PBI13721
                10.1111/pbi.13721
                8753353
                34626524
                490cde90-e0ab-4849-95a1-d31192f266bf
                © 2021 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 04 October 2021
                : 19 July 2021
                : 05 October 2021
                Page count
                Figures: 4, Tables: 0, Pages: 9, Words: 6700
                Funding
                Funded by: Key Project for Breeding Genetically Modified Organisms of China
                Award ID: 2016ZX08012‐004
                Funded by: CGIAR research program on Water, Land and Ecosystems
                Award ID: WLE
                Funded by: National Key Research and Development Program of China
                Award ID: 2017YFD0201900
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                February 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.7.0 mode:remove_FC converted:12.01.2022

                Biotechnology
                biotechnology,environmental health,agrochemical pollution,sustainable intensification

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