Wheat soil-borne mosaic: Yield loss and distribution in the US Pacific Northwest

Kroese, Duncan R.; Schonneker, L.; Bag, Sudeep; Frost, Kenneth E.; Cating, R.; Hagerty, Christina H.

doi:10.1016/j.cropro.2020.105102

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Wheat kernel number, TKW, biomass, grain yield, and RWC are reduced by biotic stresses, and the level of reduction depends on the tolerance of the cultivar, stage of infection, and the type of biotic factors (Mohapatra, Mittra, 2017;Choudhury et al, 2019;Kroese et al, 2020;Nancarrow et al, 2021). The results of our study showed a significant reduction of these parameters except for TKW, which was not significant (Table 2, Figure 1).…”

Section: Discussionmentioning

confidence: 55%

“…and reduce or increase physiological parameters (chlorophyll content, antioxidant enzymes, osmolytes, amines, etc.) (Chen et al, 2007;Pál et al, 2013;Nygren et al, 2015;Khaledi et al, 2016;Sorahinobar et al, 2016;Mohapatra, Mittra, 2017;Khaled et al, 2018;Choudhury et al, 2019;Kroese et al, 2020;Nancarrow et al, 2021). Most researchers concluded that resistant cultivars showed less reduction in their growth parameters (Nygren et al, 2015;Sorahinobar et al, 2016;Khaled et al, 2018;Wang et al, 2020;Simón et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

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Meta-analysis of common wheat physiological response to biotic stresses

Nemati

Zare

Hedayat-Evrigh

et al. 2022

Zemdirbyste-Agriculture

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Common wheat (Triticum aestivum L.), like other plants, has evolved a variety of ways to resist pathogens. However, there are some studies that reported different results at the phenotypic and physiological levels. Therefore, this meta-analysis was conducted to reveal common trends, address some controversy, and a source of heterogeneity in 19 wheat phenotypic indices. It was found that the overall response is a reduction in thousand kernel weight (TKW), kernel number, plant biomass, grain yield, relative water content (RWC), soil and plant analysis development (SPAD), and proline, and an increase in ascorbate peroxidase (APX), catalase (CAT), glutathione-S-transferase (GST), hydrogen peroxide (H 2 O 2 ), malondialdehyde (MDA), peroxidase (POX), polyphenol oxidase (PPO), superoxide dismutase (SOD), flavonoids, putrescine (PUT), salicylic acid (SA), and spermidine (SPD). However, the model was not significant for TKW, H 2 O 2 , PUT, SA, and SPD (p ≥ 0.05). The moderator analysis revealed that the effect of "cultivar" was significant on the kernel number (p ≤ 0.035) and GST (p ≤ 0.008), and the effect of "type of biotic stress" was significant on the grain yield (p ≤ 0.001), APX (p ≤ 0.0001), CAT (p ≤ 0.0009), POX (p ≤ 0.0344), flavonoids (p ≤ 0.001), and SPAD (p ≤ 0.0201). For plant biomass, the intercept effect of "cultivar" and "type of biotic stress" was significant (p ≤ 0.0187). The mixed-effect analysis addressed a source of heterogeneity in studies used in our study. However, to address additional factors affecting these parameters, some consideration for future studies is needed.

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Section: Discussionmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Meta-analysis of common wheat physiological response to biotic stresses

Nemati

Zare

Hedayat-Evrigh

et al. 2022

Zemdirbyste-Agriculture

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“…Reaction to Soilborne wheat mosaic virus (SBWMV) was evaluated in a naturally infected nursery in Walla Walla, WA, in 2019. Visual symptoms were rated seven times during the growing season, and area under the disease progress curve was calculated as described in Kroese et al (2020). Grain yield and area under the disease progress curve were analyzed using ANOVA with all effects fixed.…”

Section: Evaluation Of Abiotic Stress Tolerancementioning

confidence: 99%

Registration of ‘Cameo’ soft white winter club wheat

Garland‐Campbell

Bellinger

Carter

et al. 2022

J of Plant Registrations

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Soft white club winter wheat (Triticum aestivum L. ssp. compactum) is an important component of soft white wheat production in the Pacific Northwest of the United States. Most of the current club wheat production is in the <350 mm annual precipitation zone in central Washington, but there is interest in club wheat in the Palouse region of the United States (the counties of Whitman and Garfield in Washington and in Latah County in Idaho). Growers are continuing to grow the older club wheat cultivars ‘Cara’ and ‘Coda’, and there is a need for a new winter club wheat targeted to this region. ‘Cameo’ club wheat (Reg. no. CV‐1192, PI 699960), tested as ARS09X492‐6CBW, with awned spikes and soft white kernels, was developed using the bulk‐pedigree breeding method from the cross ARSC96059‐2/IL01‐11934//ARSC96059‐2‐0‐16. Cameo has better agronomic performance than other club wheat cultivars in trials on the Palouse, better stripe rust resistance than the club wheat ‘ARS Crescent’, tolerance to several major biotic and abiotic stressors, consistent good grain volume weight, mid‐season maturity and moderate height, excellent club wheat quality, and tolerance to low falling numbers. Cameo is not as competitive for grain yield in the traditional club wheat growing area in central Washington but is well suited to increasing the acreage of club wheat in the Palouse region of Idaho and Washington.

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“…Since then, it has spread, including to northeastern Oregon and southeastern Washington where it threatens the 2-billion-dollar production of dryland winter wheat. Significant reductions of grain yield (40%), biomass (37%) and heads per area (34%) were documented in 2017 and 2018 in association with SBWMV infection in commercial winter wheat fields (Kroese et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Detection of soil-borne wheat mosaic virus using hyperspectral imaging: from lab to field scans and from hyperspectral to multispectral data

et al. 2023

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Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.

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Wheat soil-borne mosaic: Yield loss and distribution in the US Pacific Northwest

Cited by 7 publications

References 14 publications

Meta-analysis of common wheat physiological response to biotic stresses

Meta-analysis of common wheat physiological response to biotic stresses

Registration of ‘Cameo’ soft white winter club wheat

Detection of soil-borne wheat mosaic virus using hyperspectral imaging: from lab to field scans and from hyperspectral to multispectral data

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