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Worldwide, Ascochyta blight is caused by a complex of host-specific fungal pathogens, including Ascochyta pisi, Didymella pinodes, and Didymella pinodella. The application of foliar fungicides is often necessary for disease management, but a better understanding of pathogen prevalence, aggressiveness, and fungicide sensitivity is needed to optimize control. Leaf and stem samples were obtained from 56 field pea production fields in 14 counties in North Dakota from 2017 to 2020 and isolates were collected from lesions characteristic of Ascochyta blight. Based on fungal characteristics and sequencing the ITS1-5.8S-ITS2 region, 73% of isolates were confirmed to be D. pinodes (n = 177) and 27% were A. pisi (n = 65). Across pathogens, aggressiveness was similar among some isolates in greenhouse assays. The in vitro pyraclostrobin sensitivity of all D. pinodes isolates collected from 2017 to 2020 was lower than that of the three baseline isolates. Sensitivity of 91% of A. pisi isolates collected in 2019 and 2020 was lower than the sensitivity of two known sensitive isolates. Resistance factors (Rf) from mean EC50 values of pyraclostrobin baseline/known sensitive isolates to isolates collected from 2017 to 2020 ranged from 2 to 1,429 for D. pinodes and 1 to 209 for A. pisi. In vitro prothioconazole sensitivity of 91% of D. pinodes isolates collected from 2017 to 2020 was lower than the sensitivity of the baseline isolates and 98% of A. pisi isolates collected from 2019 to 2020 was lower than the sensitivity of the known sensitive isolates. Prothioconazole Rf ranged from 1 to 338 for D. pinodes and 1 to 127 for A. pisi. Based on in vitro results, 92% of D. pinodes and 98% of A. pisi isolates collected displayed reduced-sensitivity/resistance to both fungicides when compared to baseline/known sensitive isolates. Disease control under greenhouse conditions of both pathogens provided by both fungicides was significantly lower in isolates determined to be reduced-sensitive or resistant in in vitro assays when compared to sensitive. Results reported here reinforce growers desperate need of alternative fungicides and/or management tools to fight Ascochyta blight in North Dakota and neighboring regions. [ABSTRACT FROM AUTHOR]

Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying. [ABSTRACT FROM AUTHOR]

Public–private partnerships (PPPs) can be an effective and advantageous way to accomplish extension and outreach objectives in plant pathology. The greatest opportunities for extension-focused PPPs may be in response to large-scale or emerging disease management concerns or in addressing complex issues that impact agriculture, such as climate change, digital technology, and public perception of science. The most fertile ground for forming PPPs is where the needs and strengths of the public and private sectors are complementary. Developing PPPs depends as much on professional relationships as on technical skills or contracts. Defining and making room for the success of all partners, identifying and addressing barriers to success, and earning and maintaining trust are components that contribute to the effectiveness of PPPs. Case studies in plant pathology demonstrate the positive impact PPPs can have on partners and stakeholders and provide guidance on the formation of PPPs in the future. [ABSTRACT FROM AUTHOR]

The N and P recommendations for sunflowers growers in North Dakota have not been changed in 30 yr. Twenty-two N and P rate experiments were conducted during 2014 and 2015. The objective was to determine the response of seed yield, oil concentration, and lodging to available N and P. In 2014 studies were a randomized complete block split plot with N rate as main plots and P rate as subplots. Nitrogen was applied at rates of 0, 45, 90, 134, 179, and 224 kg N ha-1. Phosphorus was applied to establish P rates of 0, 13, 26, and 39 kg P ha-1. In 2015, the field design included only 0 and 26 kg P ha-1. Experiments were taken to yield and lodging was recorded at harvest. Oil seed sunflower (17 experimental locations) was also analyzed for oil concentration. The N response of sunflower seed yield was quadratic. Increased N rate resulted in lower oil concentration in half of the oilseed experiments. Increased N rate was linearly related to increasing lodging at several sites. Phosphate fertilization had little effect on seed yield, oil concentration, and lodging, despite many of the sites having soil P levels considered 'low'. Future N rate recommendations should be based on seed yield response with increasing N and oil concentration reduction with increasing N for oilseed sunflower. A limit to maximum N rate should also be considered because of the lodging risk in this wind-plagued region. [ABSTRACT FROM AUTHOR]

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Root rots are a major concern in field pea production in North Dakota. However, it is unclear which pathogens are involved in causing these diseases. This report brings together findings from surveys conducted over four years (2004, 2005, 2008, and 2009). The 2004 and 2005 surveys were mainly aimed at establishing the importance of pea root rot in North Dakota and providing an indication of the most prevalent root rot pathogens. The 2008 and 2009 surveys involved thorough evaluation of root rot incidence and severity, and included isolations and characterization of Fusarium species associated with the root rots. Greater mean root rot incidence and severity were observed in 2009 compared to three previous years. Fusarium species were the most frequently isolated fungal species from infected pea roots, of which F. oxysporum (66.7 and 94.7 % of the fields) and F. avenaceum (71.8 and 89.5 % of the fields) were most commonly isolated in 2008 and 2009, respectively. Pathogenicity tests showed that all nine Fusarium species isolated from symptomatic roots were capable of causing root rot of pea, and isolates of F. avenaceum were the most virulent at causing root rot. Significant differences in virulence were observed among F. avenaceum isolates. The prevalence of F. avenaceum on roots of field peas, and the ability of isolates of this species to cause severe root rot, emphasizes the possibility of this pathogen to emerge as a potential risk under the current cropping practices for pulse crops in North Dakota, and potentially in other regions with similar growing conditions. [ABSTRACT FROM AUTHOR]

SUMMARY The goal of this study was to develop a tool specifically designed to identify iterative polyketide synthases (iPKSs) from predicted fungal proteomes. A fungi-based PKS prediction model, specifically for fungal iPKSs, was developed using profile hidden Markov models (pHMMs) based on two essential iPKS domains, the β-ketoacyl synthase (KS) domain and acyltransferase (AT) domain, derived from fungal iPKSs. This fungi-based PKS prediction model was initially tested on the well-annotated proteome of Fusarium graminearum, identifying 15 iPKSs that matched previous predictions and gene disruption studies. These fungi-based pHMMs were subsequently applied to the predicted fungal proteomes of Alternaria brassicicola, Fusarium oxysporum f.sp. lycopersici, Verticillium albo-atrum and Verticillium dahliae. The iPKSs predicted were compared against those predicted by the currently available mixed-kingdom PKS models that include both bacterial and fungal sequences. These mixed-kingdom models have been proven previously by others to be better in predicting true iPKSs from non-iPKSs compared with other available models (e.g. Pfam and TIGRFAM). The fungi-based model was found to perform significantly better on fungal proteomes than the mixed-kingdom PKS model in accuracy, sensitivity, specificity and precision. In addition, the model was capable of predicting the reducing nature of fungal iPKSs by comparison of the bit scores obtained from two separate reducing and nonreducing pHMMs for each domain, which was confirmed by phylogenetic analysis of the KS domain. Biological confirmation of the predictions was obtained by polymerase chain reaction (PCR) amplification of the KS and AT domains of predicted iPKSs from V. dahliae using domain-specific primers and genomic DNA, followed by sequencing of the PCR products. It is expected that the fungi-based PKS model will prove to be a useful tool for the identification and annotation of fungal PKSs from predicted proteomes. [ABSTRACT FROM AUTHOR]