Farmer Reported Pest and Disease Impacts on Root, Tuber, and Banana Crops and Livelihoods in Rwanda and Burundi

Okonya, Joshua Sikhu; Ocimati, W.; Nduwayezu, A.; Kantungeko, Déo; Niko, Nicolas; Blomme, Guy; Legg, J.P.; Kroschel, J.

doi:10.3390/su11061592

Cited by 39 publications

(31 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This makes their results difficult to compare and use in making recommendations for virus control. In the absence of an organised and consistent surveillance system, it would be difficult to manage the virus problem (Frost et al 2013;Yadav and Khurana 2015;Okonya et al 2019) and this can be a predisposing factor for further virus spread. Therefore, we provided the status in the year 2016 of the six most important potato viruses from the major potato-growing regions and linked the overall virus prevalence figures to actual virus distribution patterns while exploring options for virus control in the agronomically important cultivars in these regions.…”

Section: Discussionmentioning

confidence: 99%

Prevalence, distribution and control of six major potato viruses in Kenya

Onditi

Nyongesa

Vlugt

2020

Trop. plant pathol.

View full text Add to dashboard Cite

In most developing countries, farmers lack sufficient supply of certified or healthy potato seed tubers. Hence, they often plant their own saved ware potato tubers, a practice that is known to contribute to spread and increase the prevalence of plant viruses. In this study, we proposed options for managing the virus based on the knowledge obtained from surveys of virus prevalence and distribution in potato cultivars grown under such conditions. Potato leaf samples randomly collected from 354 farms in five major potato-growing counties in Kenya were tested for six potato viruses; potato virus Y (PVY), potato leaf roll virus (PLRV), potato virus X (PVX), potato virus M (PVM), potato virus A (PVA) and potato virus S (PVS) through DAS-ELISA. Virus prevalence in the fields was high; 72.9% of the samples were positive for at least one of the six viruses; and 55.9% showed multiple infections. A follow-up survey conducted during three consecutive seasons, in two of the five counties, revealed that virus prevalence fluctuated across seasons. This suggested that updated information on virus prevalence might be of value for designing a virus control strategy. Distribution maps showed the presence of the viruses restricted to specific geographic regions, an indication of where control efforts should be directed. Four cultivars, Sherekea, Shangi, Kenya Karibu and Asante, grown at a high virus-prevalent area, showed low values of average ELISA absorbance (OD), suggest a field resistance to the viruses. This study demonstrated that knowledge of prevalence and distribution may be of value to identify and recommend virus resistant cultivars to replace susceptible ones, especially in the virus hotspot areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Prevalence, distribution and control of six major potato viruses in Kenya

Onditi

Nyongesa

Vlugt

2020

Trop. plant pathol.

View full text Add to dashboard Cite

show abstract

“…The study was conducted in 2014 on 811 households in Rwanda and Burundi. Yields of sweet potatoes, bananas, potatoes, and cassava were measured, and crop losses due to pests and diseases were estimated at 26%, 29%, 33%, and 36%, respectively, in Rwanda and 37%, 48%, 38%, and 37%, respectively, in Burundi [21].…”

Section: Disease Predictionmentioning

confidence: 99%

A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

et al. 2019

View full text Add to dashboard Cite

This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease information, crop status, ground temperature, etc.), diagnoses crop diseases by using convolutional neural network (CNN), and predicts crop yield based on factors such as climate change, crop diseases, and others by using artificial neural network (ANN). The SCYP consists of an image preprocessing module (IPM) to determine crop diseases through the Google Vision API and image resizing, a crop disease diagnosis module (CDDM) based on CNN to diagnose the types and extent of crop diseases through photographs, and a crop yield prediction module (CYPM) based on ANN by using information of crop diseases, remaining time until harvest (based on the date), current temperature, humidity and precipitation (amount of snowfall) in the area, sunshine amount, ground temperature, atmospheric pressure, moisture evaporation in the ground, etc. Four experiments were conducted to verify the efficiency of the SCYP. In the CDMM, the accuracy and operation time of each model were measured using three neural network models: CNN, region-CNN(R-CNN), and you only look once (YOLO). In the CYPM, rectified linear unit (ReLU), Sigmoid, and Step activation functions were compared to measure ANN accuracy. The accuracy of CNN was about 3.5% higher than that of R-CNN and about 5.4% higher than that of YOLO. The operation time of CNN was about 37 s less than that of R-CNN and about 72 s less than that of YOLO. The CDDM had slightly less operation time, but in this paper, we prefer accuracy over operation time to diagnose crop diseases efficiently and accurately. When the activation function of the ANN used in the CYPM was ReLU, the accuracy of the ANN was 2% higher than that of Sigmoid and 7% higher than that of Step. The CYPM prediction was about 34% more accurate when using multiple diseases than when not using them. Therefore, the SCYP can predict farm yields more accurately than traditional methods.

show abstract

“…In recent years, worldwide per capita food consumption has increased while growth rates in global agricultural production and crop yields have declined [ 1 ]; this latter is largely due to phytosanitary problems like pests, fungi, bacteria, and, mainly in the post-harvest phase, diseases produced by oomycetes [ 2 , 3 , 4 ]. Of the diseases caused by oomycetes, root rot and fruit blight [ 5 ] are estimated to render up to 30% of the harvested vegetables and fruits inedible during postharvest handling [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Sonochemical Synthesis of 2’-Hydroxy-Chalcone Derivatives with Potential Anti-Oomycete Activity

López

Mellado

Werner³

et al. 2020

Antibiotics

View full text Add to dashboard Cite

This work reports on the synthesis of eight new 2′-hydroxy-chalcones with potential anti-phytopathogenic applications in agroindustry, among others, via Claisen–Schmidt condensation and ultrasound assisted reaction. Assays showed three chalcones with allyl moieties strongly inhibited growth of phytopathogenic oomycete Phytophthora infestans; moreover, compound 8a had a half maximal effective concentration (EC50) value (32.5 µg/mL) similar to that of metalaxyl (28.6 µg/mL). A software-aided quantitative structure–activity relationship (QSAR) analysis of the whole series suggests that the structural features of these new chalcones—namely, the fluoride, hydroxyl, and amine groups over the carbon 3′ of the chalcone skeleton—increase anti-oomycete activity.

show abstract

Farmer Reported Pest and Disease Impacts on Root, Tuber, and Banana Crops and Livelihoods in Rwanda and Burundi

Cited by 39 publications

References 32 publications

Prevalence, distribution and control of six major potato viruses in Kenya

Prevalence, distribution and control of six major potato viruses in Kenya

A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

Sonochemical Synthesis of 2’-Hydroxy-Chalcone Derivatives with Potential Anti-Oomycete Activity

Contact Info

Product

Resources

About