Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

PLoS One

Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1248 Agrosystèmes et agricultures, Gestion des ressources, Innovations et Ruralités, Castanet-Tolosan, France ; Université de Toulouse, Institut National Polytechnique de Toulouse, Ecole d'Ingénieurs de Purpan, Toulouse, France.

Published: July 2014

AI Article Synopsis

  • IPSIM (Injury Profile SIMulator) is a modeling framework designed to predict crop injury profiles based on farming practices and environmental factors.
  • The IPSIM-Wheat-Eyespot model, created using DEXi, serves as a proof of concept that estimates the risk of eyespot disease in wheat, showing reasonable predictive quality with data from 526 site-years.
  • While it doesn't aim for precise predictions, it helps rank cropping systems by risk level and aids in diagnosing commercial fields, contributing to the development of safer, more effective wheat cultivation methods.

Article Abstract

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797717PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075829PLOS

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