Estimation of area under the disease progress curves in a rice-blast pathosystem from two data points

Mukherjee, Arup; Mohapatra, N. K.; Nayak, P.

doi:10.1007/s10658-009-9568-2

Cited by 23 publications

(20 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Percent Fusarium wilt incidence by counting the number of wilted plants per plot was assessed 2-5 times (Mukherjee et al 2010) starting from the onset of wilt in the plots at 10-to 20-day intervals in all seasons and locations at vegetative, flowering-podding and full podding stages. Rate of disease development and cumulative percent Fusarium wilt incidence were used as disease parameters to determine the effects on wilt epidemic development and chickpea yield.…”

Section: Disease Assessments and Seed Yieldmentioning

confidence: 99%

Effect of integrated management on Fusarium wilt progression and grain yield of chickpea in Syria

Kemal

Abang

Imtiaz³

et al. 2015

Archives of Agronomy and Soil Science

View full text Add to dashboard Cite

Fusarium wilt, caused by Fusarium oxysporum Schlechtend.: f. sp. ciceris (Padwick) Matuo & K. Sato, is a major production problem in many countries. A study was conducted to develop an integrated management of Fusarium wilt of chickpea using genotypes, sowing dates (January as early sowing and March/April as spring sowing) and fungicide seed treatments under natural infested plots in research plots and farmers' fields 2007-2009 cropping seasons. In most cases, sowing date and fungicides did not affect disease parameters and seed yield. Chickpea genotypes showed significant differences in seed yield but different responses for disease parameters. Averaged over locations and seasons, the rate of disease development was higher in early (0.035 units day −1 ) than spring (0.023 units day −1 ) sowing. Chickpea genotypes showed different responses in affecting rate of disease development and cumulative wilt incidence in early and late sowing periods. Higher mean seed yield (1.3 t ha −1 ) was recorded in early than late sowing (1.0 t ha −1 ) of chickpea. The average seed yield reduction due to spring sowing ranged from 9% to 60% and highest yield losses were observed in FLIP-97-706 and Ghab-3. This study showed that integrating January sowing with genotypes having good levels of resistance for Fusarium wilt and Ascochyta blight helps farmers to narrow chickpea yield gaps in Syria.

show abstract

Section: Disease Assessments and Seed Yieldmentioning

confidence: 99%

Effect of integrated management on Fusarium wilt progression and grain yield of chickpea in Syria

Kemal

Abang

Imtiaz³

et al. 2015

Archives of Agronomy and Soil Science

View full text Add to dashboard Cite

show abstract

“…Similar results were observed in a resistance screening with rice -Pyricularia grisea pathosystem, where a two data points AUDPC (initiation and end of the epidemic) provided similar information than the AUDPC obtained from 12 assessment points (Mukherjee et al, 2010).…”

Section: Discussionsupporting

confidence: 81%

Yield losses of soybean due to target spot (<i>Corynespora cassiicola</i>), its genetic and chemical management

Molina¹,

Pablo²

View full text Add to dashboard Cite

I would like to thank Dr. Lilian Amorim, a wonderful adviser and person, for giving me the chance to be part of her lab, for the support, for sharing her knowledge, points of view, and warm friendship. Thanks for teaching me "how to advise", and being such an inspiring researcher and group leader. I also want to thank, Dr. Cláudia Godoy (from Embrapa soybean), for her guidance throughout this project.

show abstract

“…In this sense, ANN utilization represents an interesting alternative, since it has allowed for the selection of putative sources of resistance with a reduced number of evaluations. Also, according to Mukherjee et al (2010), when evaluations are frequent during the outbreak they can inadvertently affect disease progress, given anthropic interference.…”

Section: Resultsmentioning

confidence: 99%

“…Jeger and Viljanen-Rollinson (2001) proposed a method for calculating the AUDPC with only two evaluations for the wheat pathosystem -Puccinia striiformis f. sp. tritici, which was later validated by Mukherjee et al (2010) for the rice pathosystemPyricularia grisea. Nonetheless, Jeger and ViljanenRollinson (2001) reinforce that the methodology has three assumptions that should be satisfied: first, the resistance must be expressed as the disease rate and not as the absence of symptoms; second, the disease evaluated has to be present in all plants during the same timeframe; and third, the disease progress has to be continuous.…”

Section: Introductionmentioning

confidence: 91%

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Alves

Tomaz

Laurindo

et al. 2017

Sci. agric. (Piracicaba, Braz.)

View full text Add to dashboard Cite

Artificial neural networks (ANN) are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications.In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC) for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes.However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

show abstract

Estimation of area under the disease progress curves in a rice-blast pathosystem from two data points

Cited by 23 publications

References 13 publications

Effect of integrated management on Fusarium wilt progression and grain yield of chickpea in Syria

Effect of integrated management on Fusarium wilt progression and grain yield of chickpea in Syria

Yield losses of soybean due to target spot (<i>Corynespora cassiicola</i>), its genetic and chemical management

Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

Contact Info

Product

Resources

About