Identification of New Hosts of Pseudocercospora fijiensis Suggests Innovative Pest Management Programs for Black Sigatoka Disease in Banana Plantations

Vázquez-Euán, Roberto; Chí-Manzanero, Bartolomé; Hernández-Velázquez, Ioreni Margarita; Tzec-Simá, Miguel; Islas‐Flores, Ignacio; Martínez-Bolaños, Luciano; Garrido-Ramírez, Eduardo Raymundo; Canto-Canché, Blondy

doi:10.3390/agronomy9100666

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…In this regard, in the climate of the South Pacific of Mexico, Black Sigatoka showed that the greatest damage (12 to 25% severity) occurs during the months of June to December, which coincides with the season of greatest rainfall [54]. In this period, symptoms appear in a state of spot between leaf nos.…”

Section: Discussionmentioning

confidence: 98%

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

et al. 2022

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Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis.

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Section: Discussionmentioning

confidence: 98%

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

et al. 2022

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“…Studies in the topic of pests and diseases were related to the incidence and severity in cultivation of heliconia caused by pathogens such as F. oxysporum f. sp. cubense, Pseudocercospora fijiensis; nematodes of the genus Meloidogyne, Helicotylenchus erythrinae, H. crenacauda, H. dihystera, Meloidogyne incognita, Pratylenchus zeae and Rotylenchulus reniformis, defoliators such as Cornops frenatum frenatum and mites, particularly specimens of Tropicoseius venezuelensis Baker & Yunker (Castro et al, 2008;Lemos et al, 2010;Sardinha et al, 2012;Sobrinho et al, 2012;Britto et al, 2015;Vázquez-Euán et al, 2019). Despite the affectations some authors recommend that the use of synthetic insecticides should be done only in extreme cases (Lemos et al, 2010).…”

Section: Productionmentioning

confidence: 99%

Global vision of heliconias research as cut flower: a review

et al. 2020

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It is relevant to systematize the global knowledge generated about heliconia due to its economic importance in tropical floriculture and the cut flower market. The objective was to explore the results generated in scientific research related to the cultivation of heliconias as cut flower in terms of its methodological approach, research axes, and spatial distribution in terms of the contributions by country. A bibliographic search was conducted in Scopus, Web of science and SciELO based on the keywords “heliconia” and “tropical flowers”. Thereafter, a content analysis of the documents was conducted with the Nvivo software using a priori categories in terms of methodological approach, research axes, year, countries, and cultivars or varieties. The results indicate that the scientific contributions are mostly under a quantitative approach (98.1%). The investigations mainly address productive aspects (74.5%), post-harvest aspects (16.1%), market (6.7%) while industry-used products (2.5%) are scarcely addressed. The countries that make the most contributions are: Brazil (54.5%), Colombia (15.4%) and Mexico (10.9%). The most important species are: H. psittacorum, H. bihai, H. spathocircinata, H. rostrata and H. wagneriana. These results suggest conducting research to identify problems from the physiological, environmental, productive, and economic processes, as well as considering the strategies of farmers as a priority. This must be approached from paradigms where social actors (farmers), their development and the impacts of their social tasks on the cultivation of heliconias are considered the main axis.

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“…Another major challenge for the global production of Musaceae species is the development of cultivars resistant to biotic stressors, represented by their primary pests, the banana root borer ( Cosmopolites sordidus ) and the nematodes Meloidogyne spp., Pratylenchus coffeae , and Radopholus similis [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ], and disease-causing pathogens, including banana bunchy top virus (BBTV) [ 20 , 21 ], Xanthomonas vasicola pv. musacearum causing bacterial wilt [ 22 , 23 , 24 , 25 ], Pseudocercospora fijiensis causing black Sigatoka [ 26 , 27 , 28 , 29 ], and Fusarium oxysporum f. sp. cubense (FOC) causing Fusarium wilt [ 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Improvements in the Resistance of the Banana Species to Fusarium Wilt: A Systematic Review of Methods and Perspectives

Rocha

Soares

Nascimento

et al. 2021

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The fungus Fusarium oxysporum f. sp. cubense (FOC), tropical race 4 (TR4), causes Fusarium wilt of banana, a pandemic that has threatened the cultivation and export trade of this fruit. This article presents the first systematic review of studies conducted in the last 10 years on the resistance of Musa spp. to Fusarium wilt. We evaluated articles deposited in different academic databases, using a standardized search string and predefined inclusion and exclusion criteria. We note that the information on the sequencing of the Musa sp. genome is certainly a source for obtaining resistant cultivars, mainly by evaluating the banana transcriptome data after infection with FOC. We also showed that there are sources of resistance to FOC race 1 (R1) and FOC TR4 in banana germplasms and that these data are the basis for obtaining resistant cultivars, although the published data are still scarce. In contrast, the transgenics approach has been adopted frequently. We propose harmonizing methods and protocols to facilitate the comparison of information obtained in different research centers and efforts based on global cooperation to cope with the disease. Thus, we offer here a contribution that may facilitate and direct research towards the production of banana resistant to FOC.

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Identification of New Hosts of Pseudocercospora fijiensis Suggests Innovative Pest Management Programs for Black Sigatoka Disease in Banana Plantations

Cited by 9 publications

References 20 publications

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Global vision of heliconias research as cut flower: a review

Improvements in the Resistance of the Banana Species to Fusarium Wilt: A Systematic Review of Methods and Perspectives

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