Figure 1Figure 2 Pectolytic Dickeya spp. and Pectobacterium spp. are commercially important seed-borne bacteria of potato (Solanum tuberosum) that cause blackleg, soft rot and aerial stem rot (Potrykus et al., 2014;Stevenson et al., 2001). Dickeya and Pectobacterium spp. have been recovered from potato production fields in parts of the USA (Dickey, 1979;Ma et al., 2007) Five seven-week-old cv. 'Russet Norkotah' potato plants were woundinoculated by inserting a sterile 18-gauge needle just above a central leaf axil at a depth of 1 mm. A 100 μl drop of inoculum (10 6 cfu/ml) was placed on the wound. Plants were exposed to a 24 h leaf wetness period (90 to 100% relative humidity in a mist chamber) until symptom expression (Figs. 1, 2), and lesions were measured. All three inoculated plants exhibited blackening of the stem and in advanced stages, drying and cracking (Figs. 1, 2). Upon desiccation, the lesions became shriveled and turned dark brown to black. Water-inoculated controls were nonsymptomatic (Figs. 1, 2). The reisolated bacteria caused pitting on crystal violet pectate agar (Hélias et al., 2012) and exhibited the same morphology as original cultures on NBY, and were confirmed as D. dianthicola using 16S rRNA and acnA coding sequences, and P. wasabiae using rRNA, acnA and mdh coding sequences, fulfilling Koch's postulates. To our knowledge, this is the first report of D. dianthicola and P. wasabiae causing aerial stem rot of potato in Michigan. References

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Alternaria leaf spot (ALS), caused by Alternaria spp., can occur wherever sugarbeet is grown. Infection by Alternaria spp. and disease management has historically been considered a minor issue in sugarbeet production in the United States. An increase of both incidence and severity in 2016 of ALS high enough to cause yield loss has been observed in Michigan. With a renewed need to consider potential management of this disease, the sensitivity was determined for populations of Alternaria spp. to three classes of fungicides currently labeled for management of leaf spot on sugarbeet, including demethylase inhibitor (DMI), quinone outside inhibitor (QoI), and organo-tin fungicides. Leaves with symptoms of ALS were sampled from sugarbeet fields in east-central Michigan and southwestern Ontario, Canada. Monoconidial isolates were obtained to determine sensitivity to each fungicide class above. A spiral gradient dilution method was used to estimate the fungicide effective concentration (in milligrams per liter) that caused a 50% inhibition of fungal growth in vitro for all isolates. Significant temporal shifts were detected in the frequencies of sensitivity phenotypes to DMI and QoI but not organo-tin fungicides from 2016 through 2017. Individual isolates of Alternaria spp. were recovered with cross-resistance to DMI and multiple resistance to DMI, QoI, and triphenyltin hydroxide fungicides. To our knowledge, this is the first report of a fungus other than Cercospora beticola with resistance to organo-tin fungicides. Fungicide sensitivity monitoring indicates that an effective integrated disease management approach combining fungicide efficacy trials and monitoring pathogen biology is essential for developing effective resistance management recommendations.

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

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