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  • 1
    Electronic Resource
    Electronic Resource
    Temperature Dependent Seed Transmission of Stagonospora nodorum in Wheat (2000)
    Springer
    European journal of plant pathology 106 (2000), S. 837-842 
    Details
    ISSN: 1573-8469
    Keywords: Triticum aestivum ; Septoria nodorum ; Phaeosphaeria nodorum ; epidemiology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Abstract The transmission of Stagonospora nodorum from four naturally infected winter wheat seedlots was quantified in controlled environment germination chambers at 9, 13, 17, 21, and 25 °C. Seedlings were harvested when the second leaf began to emerge. Coleoptiles and first seedling leaves were examined for the presence of lesions caused by S. nodorum. First leaves were incubated on Bannon's medium for 2 weeks, after which they were examined for pycnidia of S. nodorum. Transmission to the coleoptile occurred at all temperatures, but decreased from 100% to 72% as temperature increased from 9 to 25 °C. Transmission to the first leaf was less, dropping from 37% to 2% as temperature increased from 9 to 25 °C. At least 44% of infected first leaves were symptomless at all temperatures, with 96% of infected leaves showing no symptoms at 25 °C. Transmission to seedling leaves occurred over a broad temperature range. Under the high densities at which wheat is sown, a significant number of infected seedlings per unit area may originate from relatively low initial seed infection levels and transmission efficiencies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008723823196
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    Paper (German National Licenses)
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  • 2
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    Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models (2021)
    Public Library of Science
    Details
    Publication Date: 2021-03-15
    Description: Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.
    Print ISSN: 1553-734X
    Electronic ISSN: 1553-7358
    Topics: Biology , Computer Science
    Published by Public Library of Science
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  • 3
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    A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States (2021)
    Springer Nature
    Details
    Publication Date: 2021-09-21
    Description: Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Springer Nature
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