SIPPOM-WOSR, Simulator for Integrated Pathogen POpulation Management: a tool to help design and evaluate sustainable strategies to control Phoma stem canker on winter oilseed rape at the regional scale

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SIPPOM-WOSR, Simulator for Integrated Pathogen POpulation Management: a tool to help design and evaluate sustainable strategies to control Phoma stem canker on winter oilseed rape at the regional scale

Description

Abstract: Phoma stem canker, also known as blackleg, is a major disease of oilseed rape. Among the different means to control the disease – chemical treatments, agronomic practices and plant genetic resistances – the use of resistant cultivars is the most efficient. Winter oilseed rape cultivars have two types of resistance to Phoma stem canker, either specific or quantitative. New specific resistances are extremely efficient but may lack durability. Combining genetic, cultural and chemical control methods at the multiple-years and regional scales could help contain Phoma stem canker and preserve the efficiency of specific resistances, while ensuring economic profit for farmers and satisfying the environmental and toxicological exigencies of Integrated Crop Management. Given the considered scales and the number of technical operations that have to be taken into account, it is highly difficult to test disease management strategies using traditional field experiments. A model has been developed to evaluate the agronomic, economic and environmental performances of spatially distributed cropping systems: SIPPOM-WOSR, a Simulator for Integrated Pathogen Population Management, for Winter OilSeed Rape. SIPPOM consists of 5 sub-models simulating i) primary inoculum production, ii) ascospore dispersal, iii) crop growth and attainable yield, iv) dynamics of pathogen population genetic structure, and v) infection and relative yield loss. The output variables are disease severity indices and the associated yield losses, actual yields, gross margins, energetic costs of cultural practices and Treatment Frequency Indices. It also calculates the genetic structure of pathogen populations depending on four evolutionary forces or genetic mechanisms: migration, selection, recombination, and the allele effect. A sensitivity analysis has been carried out to study the sensitivity of the sub-models to parameter variations. It showed that SIPPOM can be confidently used to rank contrasted integrated control strategies. The evaluation of each sub-model revealed correct predictive quality. A comparison between simulated and observed data during the loss of efficacy period – 1994 to 2000 – of the Rlm1 specific resistance gene in the centre of France was satisfactory. Nevertheless, the results underlined the interest of introducing virulence costs in SIPPOM. Further simulations were carried out on the 2004-2008 period to assess the behaviour of SIPPOM for realistic field spatial distributions and cropping systems. Results showed that the disease index calculation should be adjusted to improve SIPPOM’s predictive quality. After improvements, strategies minimising the severity of Phoma stem canker and the risk of specific resistance loss of efficacy will be simulated.

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