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Conference object . 2022
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Reducing pesticide use in arable fields through cropping system re-design:what impact on farm productivity and profitability?

Authors: Nandillon, Romain; Guinet, Maé; Munier-Jolain, Nicolas;

Reducing pesticide use in arable fields through cropping system re-design:what impact on farm productivity and profitability?

Abstract

Due to their impacts on biodiversity and human health, reducing pesticide use is a key stepfor transitioning to sustainable farming systems (Matson et al, 1997; Storkey et al, 2012).Following the European SUD Directive, the Ecophyto plan set the objective of halving pesticideuse in France, primarily by 2018, but thereafter postponed to 2025 (ECOPHYTO II+ Plan,Ministry of Ecological and Solidarity Transition, 2018).The Ecophyto plan launched the DEPHY network, based on about 3.000 farms engaged in thedemonstration that reducing pesticide use is possible without impairing farm profitability.Farms are coordinated in groups of 10-15 farms, and detailed practices data are collected andgathered into a national database (DEPHY FARM network, 2018).Previous research based on this database has shown that in a majority of sites, low pesticideuse was not conflicting with profitability nor productivity (Lechenet et al, 2017). Croppingsystems with low pesticide use were associated with combinations of preventive measuresthat varied across production situations and across farms (Lechenet et al, 2016). These results,based on a synchronic comparison of farms, tend to demonstrate that using little amount ofpesticide is possible in most cases, but research remains to address the transition from highto low pesticide use, based on a diachronic approach.With a large range of data (more than 1 300 farms having joined the network since at least 3years), we are performing detailed analysis of the evolution of pesticide use over time, and itsrelation to the evolution of farm practices implementation. We consider how the productioncontext (soil type, climate, access to specific markets) influences both practices and pesticideuse.For each site, we computed a range of variables describing (i) the production situation, (ii) thecropping system and pesticide use at the enrolment in the network, and (iii) the changes incropping system and pesticide use after a few years. The Treatment Frequency Index (acommonly used index for estimating the pesticide use dependency of a farm (Brunet et al,2008)) is used as a metric of pesticide use. Variables describing cropping systems includedescriptors of practices expected to have an effect on pest pressure, and therefore onpesticide use (e.g. among others: soil tillage strategy, crop sequence, sowing dates,Fertilization level, mechanical weeding, etc. (Davis et al, 2012; Lechenet et al, 2016). A CART(Classification And Regression Tree) method is used to analyse changes in pesticide use (DTFI= TFI final – TFIinitial) as a function of all variables describing (i) changes in cropping systemsand crop management, (ii) initial cropping systems and (iii) production situations. Themethods allow to identify which evolution of (combinations of) practices are associated with325a reduction or an increase in pesticide use, taking into account both specificities of productionsituations and initial cropping systems.Analyses are currently in progress. At the date of the conference, we will present the results.We anticipate that major changes in practices allowing to reduce pesticide use will be inaccordance with strategies with low reliance on pesticides identified by Lechenet et al. (2016),namely the diversity of the crop sequence, the soil tillage strategy, the moderation offertilization, the use of resistant cultivars, the sowing date, the reduction of pesticide dose ateach application, etc. We intend to complement this study by analyzing the consequences ofthese changes in cropping system and management on other sustainability indicators(productivity, profitability at the farm level, work load, environmental impacts)

Country
France
Keywords

[SDV] Life Sciences [q-bio]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average