Disease variation and chemical control of Ramularia leaf spot in sugar beet

Thach, Tine; Munk, Lisa; Hansen, Anne Lisbet; Jørgensen, Lise Nistrup

doi:10.1016/j.cropro.2013.04.010

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…In our experiment, in the period between June and July 2012, precipitation rate was surplus, but between August and September -deficient. Contrary to Thach et al (2013) findings, we established strong negative correlation between precipitation rate and ramularia leaf spot severity (r = −0.715*) in the period between June and September (11 weeks). Increase of sum of active temperatures did not correlate with the incidence of cercospora and ramularia, but was favourable for powdery mildew severity (r = 0.423).…”

Section: Methodscontrasting

confidence: 51%

“…In October, average air temperature was 8.3°C or by 1.3°C higher than usual. In Denmark Thach et al (2013) found that the severity of the diseases strongly depended on the increase of precipitation rates in the periods between May and September (weeks 18-39). In our experiment, in the period between June and July 2012, precipitation rate was surplus, but between August and September -deficient.…”

Section: Methodsmentioning

confidence: 99%

“…Thach et al (2013) found that fungicide compounds (active ingredients) pyraclostrobin, epoxiconazole, difenoconazole and propiconazole gave effective control against ramularia. Positive net yield responses (on average 0.7-2.2 t ha -1 ) were found in 9 out of 11 seasons.…”

Section: And 7)mentioning

confidence: 99%

“…In some years, this disease disperses widely, sugar beet leaves dry-out, sugar beet root yield and sugar yield drastically decrease (Žemės ūkio augalų kenkėjai…, 2002). Thach et al (2013) concluded that Ramularia beticola in sugar beet was a serious disease in 5 out of 11 seasons. The severity and significance of the disease varied depending on the rainfall rate, particularly in two specific weeks in July and September.…”

Section: Introductionmentioning

confidence: 99%

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Efficacy of fungicides in sugar beet crops

Avižienytė

Brazienė

Romaneckas

et al. 2016

Zemdirbyste-Agriculture

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Cercospora and ramularia leaf spot are the most damaging fungal diseases of sugar beet in Europe. The aim of the current study was to evaluate the severity of fungal diseases and the efficacy of various fungicides and their doses in sugar beet crops in Lithuania. A short-term field experiment was carried out in 2012 and 2013 at Rumokai Experimental Station, Lithuanian Research Centre for Agriculture and Forestry on a moderately heavy loam HapliEpihypogleyic Luvisol (LVg-p-w-ha). Fungicides Impact (a.i. flutriafol), Opus (a.i. epoxiconazole), Artea (a.i. propikonazole + cyproconazole), Folicur (a.i. tebuconazole), Opera N (a.i. pyraclostrobin + epoxiconazole) were applied as a single application at the beginning of the disease occurrence (in August) and as a double application, with one spray at the beginning of the disease occurrence (in August) plus a second spray one month later (in September). The experimental results showed that the main fungal diseases in the sugar beet crops tested were Cercospora beticola and Ramularia beticola. Erysiphe betae was of minor severity. The application of fungicides significantly decreased the severity of cercospora and ramularia leaf spots by on average 19.8-81.8% and 71.8-91.8%, respectively. The most effective fungicides were Opus (a.i. epoxiconazole) and Impact (a.i. flutriafol). A single or double application of all the investigated fungicides had essentially the same effect. The most effective against powdery mildew was fungicide Opus, treated twice. Various active ingredients of the fungicides had insignificant effect on sugar beet yield and sucrose content in the roots. A single application of the fungicide Artea 0.5 L ha -1 (a.i. propiconazole + cyproconazole) and a double application Opus 1.0 L ha -1 (a.i. epoxiconazole) and Folicur 1.0 L ha -1 (a.i. tebuconazole) significantly decreased the content of impurities (potassium, sodium, alpha amino nitrogen) in the sugar beet roots compared with the untreated control treatment.

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Section: Methodscontrasting

confidence: 51%

Section: Methodsmentioning

confidence: 99%

Section: And 7)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficacy of fungicides in sugar beet crops

Avižienytė

Brazienė

Romaneckas

et al. 2016

Zemdirbyste-Agriculture

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“…PHOMLS or RAMULS, can be achieved by considering more regions/images of these diseases. However, these two diseases have been rare in sugar beet fields in Germany in recent times, although they have been reported to be an economic problem in other periods of time (Maerlaender et al, 2003) and other regions (Thach et al, 2013). The shortage of sufficient training data for PHOMLS and the misclassification of phoma leaf spots as cercospora leaf spots or ramularia leaf spots are probably the reasons for the low accuracy for PHOMLS in the evaluation step.…”

Section: Discussionmentioning

confidence: 99%

Automated identification of sugar beet diseases using smartphones

et al. 2017

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Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.

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Etiology, Epidemiology, and Management of Sugar Beet Diseases

Esh¹,

Taghian²

2022

Sugar Beet Cultivation, Management and Processing

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Disease variation and chemical control of Ramularia leaf spot in sugar beet

Cited by 12 publications

References 28 publications

Efficacy of fungicides in sugar beet crops

Efficacy of fungicides in sugar beet crops

Automated identification of sugar beet diseases using smartphones

Etiology, Epidemiology, and Management of Sugar Beet Diseases

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