PMID- 34153290
OWN - NLM
STAT- MEDLINE
DCOM- 20210927
LR  - 20210927
IS  - 1096-0325 (Electronic)
IS  - 0040-5809 (Linking)
VI  - 141
DP  - 2021 Oct
TI  - Improving pest monitoring networks using a simulation-based approach to 
      contribute to pesticide reduction.
PG  - 24-33
LID - S0040-5809(21)00047-2 [pii]
LID - 10.1016/j.tpb.2021.06.002 [doi]
AB  - Conventional pest management mainly relies on the use of pesticides. However, the 
      negative externalities of pesticides are now well known. More sustainable 
      practices, such as Integrated Pest Management, are necessary to limit crop damage 
      from pathogens, pests and weeds in agroecosystems. Reducing pesticide use 
      requires information to determine whether chemical treatments are really needed. 
      Pest monitoring networks (PMNs) are key contributors to this information. 
      However, the effectiveness of a PMN in delivering relevant information about 
      pests depends on its spatial sampling resolution and its memory length. The 
      trade-off between the monitoring efforts and the usefulness of the information 
      provided is highly dependent on pest ecological traits, the damage they can cause 
      (in terms of crop losses), and economic drivers (production costs, agriculture 
      product prices and incentives). Due to the high complexity of optimising PMNs, we 
      have developed a theoretical model that belongs to the family of Dynamic Bayesian 
      Networks in order to compare several PMNs performances. This model links the 
      characteristics of a PMN to treatment decisions and the resulting pest dynamics. 
      Using simulation and inference tools for graphical models, we derived the 
      proportion of impacted fields, the number of pesticide treatments and the overall 
      gross margins for three types of pest with contrasting levels of endocyclism. The 
      term "endocyclic" refers to an organism whose development is mostly restricted to 
      a field and highly depends on the inoculum present in the considered field. The 
      presence of purely endocyclic pests at a given time increases the probability of 
      reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The 
      simulation analysis considered ten scenarios: an expected margin-based strategy 
      with a spatial resolution of four PMNs and two memory lengths (one year or eight 
      years), as well as two extreme crop protection strategies (systematic treatments 
      on all fields and systematic no treatment). For purely and mainly endocyclic 
      pests (e.g. soil-borne pathogens and most weeds, respectively), we found that 
      increasing the spatial resolution of PMNs made it possible to significantly 
      decrease the number of treatments required for pest control. Taking past 
      observations into account was also effective, but to a lesser extent. PMN 
      information had virtually no influence on the control of non-endocyclic pests 
      (such as flying insects or airborne plant pathogens) which may be due to the 
      spatial coverage addressed in our study. The next step is to extend the analysis 
      of PMNs and to integrate the information generated by PMNs into sustainable pest 
      management strategies, both at the field and the landscape level.
CI  - Copyright (c) 2021 Elsevier Inc. All rights reserved.
FAU - Cros, Marie-Josee
AU  - Cros MJ
AD  - INRAE, Universite de Toulouse, UR MIAT, F-31320 Castanet-Tolosan, France. 
      Electronic address: Marie-Josee.Cros@inrae.fr.
FAU - Aubertot, Jean-Noel
AU  - Aubertot JN
AD  - INRAE, INPT, Universite de Toulouse, UMR AGIR, F-31320 Castanet-Tolosan, France.
FAU - Gaba, Sabrina
AU  - Gaba S
AD  - INRAE, USC 1339, Centre d'Etudes Biologiques de Chize, F-79360 Villiers-en-Bois, 
      France; CNRS, Universite La Rochelle, UMR 7372, Centre d'Etudes Biologiques de 
      Chize, F-79360 Beauvoir-sur-Niort, France.
FAU - Reboud, Xavier
AU  - Reboud X
AD  - INRAE, AgroSup Dijon, Universite Bourgogne Franche-Comte, Agroecologie, F-21000 
      Dijon, France.
FAU - Sabbadin, Regis
AU  - Sabbadin R
AD  - INRAE, Universite de Toulouse, UR MIAT, F-31320 Castanet-Tolosan, France.
FAU - Peyrard, Nathalie
AU  - Peyrard N
AD  - INRAE, Universite de Toulouse, UR MIAT, F-31320 Castanet-Tolosan, France.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210618
PL  - United States
TA  - Theor Popul Biol
JT  - Theoretical population biology
JID - 0256422
RN  - 0 (Pesticides)
SB  - IM
MH  - Agriculture
MH  - Animals
MH  - Bayes Theorem
MH  - Insecta
MH  - Pest Control
MH  - *Pesticides
OTO - NOTNLM
OT  - Decision rules
OT  - Dynamic Bayesian Network
OT  - Endocyclism
OT  - Gibbs sampling
OT  - Pest monitoring network
OT  - Theoretical modelling
EDAT- 2021/06/22 06:00
MHDA- 2021/09/28 06:00
CRDT- 2021/06/21 20:12
PHST- 2020/04/17 00:00 [received]
PHST- 2021/06/01 00:00 [revised]
PHST- 2021/06/05 00:00 [accepted]
PHST- 2021/06/22 06:00 [pubmed]
PHST- 2021/09/28 06:00 [medline]
PHST- 2021/06/21 20:12 [entrez]
AID - S0040-5809(21)00047-2 [pii]
AID - 10.1016/j.tpb.2021.06.002 [doi]
PST - ppublish
SO  - Theor Popul Biol. 2021 Oct;141:24-33. doi: 10.1016/j.tpb.2021.06.002. Epub 2021 
      Jun 18.