Improvement and validation of a pea crop growth model to simulate the growth of cultivars infected with Ascochyta blight (Mycosphaerella pinodes)

May, Christophe Le; Schoeny, Alexandra; Tivoli, Bernard; Ney, Bertrand

doi:10.1007/s10658-004-5272-4

Cited by 27 publications

(14 citation statements)

References 17 publications

(29 reference statements)

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“…This would then determine whether the underlying model could be adapted and adopted elsewhere to prevent primary infection of the chickpea crop by targeted fungicide sprays to kill the ascospores early in the season. It is obvious that in systems where infected seed, stubble, pycnidia and potentially ascospores can initiate ascochyta blight, disease forecasting is bound to become more complex as exemplified by the models developed for M. pinodes in France (Bé asse et al 2000;Le May et al 2005). It is probably safe to say that for those systems we may not know enough about the relative importance of each of these sources, which may be highly variable depending upon the location and year.…”

Section: Resultsmentioning

confidence: 99%

“…Resistance to M. pinodes was found to be positively correlated with lodging resistance, and both lodging and mycosphaerella blight were negatively correlated with the proportion of xylem, lignin and fibre content of pea stems . Le May et al (2005) developed a simulation model for the growth of pea infected with mycosphaerella blight by incorporating architectural features such as stem height, branching ability and lodging resistance into the model.…”

Section: Cultural Factors and Host Resistancementioning

confidence: 99%

“…Using and building upon a disease-coupled crop growth model published by Bé asse et al (2000), Le May et al (2005) developed an improved model to predict the impact of ascochyta blight in pea on yield components by incorporating a combination of disease progression in the canopy (number of nodes affected by the disease) and the structure of the canopy (leaf area index profile). For doing so, they first estimated the contribution of each node to radiation absorption, then calculated the reduction in contribution of each node due to disease and finally combined the individual contributions which allowed them to estimate crop growth.…”

Section: Modellingmentioning

confidence: 99%

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Comparison of the epidemiology of ascochyta blights on grain legumes

2007

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Asochyta blights of grain legumes are caused by fungal pathogens in the genus Ascochyta. Different species infect the different legume species, and in pea three species including Phoma medicaginis var. pinodella have been implicated in ascochyta blight. The impact of the diseases varies between crops, countries, seasons and cropping systems, and yield loss data collected under welldefined conditions is scarce. However, ascochyta blights are considered major diseases in many areas where legumes are grown. Symptoms appear on all aerial parts of the plant, and lesions are similar for most of the species, except for M. pinodes and P. medicaginis var. pinodella. Infected seed, stubble and/or air-borne ascospores are major sources of primary inoculum. Their importance varies between species and also between regions. All Ascochyta spp. produce rain-splashed conidia during the cropping season which are responsible for the spread of the disease within the crop canopy. Only in pea are ascospores involved in secondary disease spread. Limited data suggests that Ascochyta spp. may be hemibiotrophs; however, toxins characteristic for necrotrophs have been isolated from some of the species. Modelling of ascochyta blights is still in the developmental stage and implementation of such models for disease forecasting is the exception.

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Section: Resultsmentioning

confidence: 99%

Section: Cultural Factors and Host Resistancementioning

confidence: 99%

Section: Modellingmentioning

confidence: 99%

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Comparison of the epidemiology of ascochyta blights on grain legumes

2007

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“…Part of this differential response might be explained by the time-lag between the two methods of disease assessment: stipules being assessed before or during flowering (after this stage, physiological senescence biases disease assessment of the lowest nodes), whereas pods and stems were assessed later, at the beginning of physiological maturity. Ascochyta blight affects yield either indirectly through reduction of photosynthetic leaf area and the photosynthetic efficiency of the remaining green leaf area (Garry et al 1998a), leading to a reduced biomass production (Béasse et al 2000;Le May et al 2005), or directly through pod infection (Béasse et al 1999). Consequently, although intercropping had almost no effect on disease development on stipules, which represent the main photosynthetic organs of the compound leaf in semi-leafless pea cultivars, it may limit direct yield loss by limiting disease development on pods.…”

Section: Discussionmentioning

confidence: 99%

Effect and underlying mechanisms of pea-cereal intercropping on the epidemic development of ascochyta blight

et al. 2009

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Field experiments were conducted in western France for two consecutive years to investigate the effect of pea-cereal intercropping on ascochyta blight, a major constraint of field pea production world-wide. Disease pressure was variable in the experiments. Intercropping had almost no effect on disease development on stipules regardless of disease pressure. In contrast, disease severity on pods and stems was substantially reduced in the pea-cereal intercrop compared to the pea monocrop when the epidemic was moderate to severe. Therefore, a pea-cereal intercrop could potentially limit direct yield loss and reduce the quantity of primary inoculum available for subsequent pea crops. Disease reduction was partially explained by a modification of the microclimate within the intercrop canopy, in particular, a reduction in leaf wetness duration during and after flowering. The effect of intercropping on splash dispersal of conidia was investigated under controlled conditions using a rainfall simulator. Total dispersal was reduced by 39 to 78% in pea-wheat canopies compared to pea canopies. These reductions were explained by a reduction in host plant density and a barrier or relay effect of the non-host plants.

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“…The disease, mainly caused by Mycosphaerella pinodes, infects all aerial organs of the plant (leaves, stems, flowers, pods) and can cause yield losses of up to 75% when conditions are favourable for an epidemic (Lawyer 1984). The disease affects yield either indirectly through reduction of biomass production (Béasse et al 2000;Garry et al 1998;Le May et al 2005), or directly through pod infection (Béasse et al 1999). The relative importance of these two effects depends on the location of the symptoms on the plant and therefore on the precocity and intensity of the epidemic.…”

Section: Introductionmentioning

confidence: 99%

Assessment of airborne primary inoculum availability and modelling of disease onset of ascochyta blight in field peas

Schoeny¹,

Jumel²,

Rouault³

et al. 2007

Eur J Plant Pathol

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Ascochyta blight is a serious disease affecting field peas. In France, disease management relies mainly on scheduled chemical applications without taking into account the actual disease risk. A better understanding of the factors affecting disease onset would therefore help in the timing of the first application. Field experiments involving eight sowing dates between mid-September and mid-December were conducted for two consecutive years. The seasonal dynamics of airborne inoculum were investigated through trap plants. The weekly availability of airborne primary inoculum was extremely low during autumn and winter and was partially influenced by mesoclimatic conditions. Disease onset occurred between mid-October and early March depending on the sowing date. Generally, the later the sowing date, the longer the period between sowing and disease onset. This was due to an increase in the period between sowing and emergence. Disease onset was observed 14-35 days after emergence. A disease onset model based on the calculation of weather-dependent daily infection values (DIVs) was established, assuming that disease onset occurs once the temperature and moisture requirements for incubation are met. Cumulative daily infection values (cDIVs) were determined by sowing date and experiment through addition of consecutive DIVs between emergence and disease onset. A frequency analysis of cDIVs was performed to determine the 10th and 90th percentiles of the distribution. An analysis of the observed and predicted values showed that observed disease onset dates were almost always included in the forecast window defined by these two percentiles. This study is the first attempt to predict ascochyta blight onset in field peas and should contribute to development of a more rational fungicide application strategy.

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Improvement and validation of a pea crop growth model to simulate the growth of cultivars infected with Ascochyta blight (Mycosphaerella pinodes)

Cited by 27 publications

References 17 publications

Comparison of the epidemiology of ascochyta blights on grain legumes

Comparison of the epidemiology of ascochyta blights on grain legumes

Effect and underlying mechanisms of pea-cereal intercropping on the epidemic development of ascochyta blight

Assessment of airborne primary inoculum availability and modelling of disease onset of ascochyta blight in field peas

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