Cassava Mosaic Disease Research Articles

Behavioural and physiological responses of whitefly, Bemisia tabaci, to virus infection in cassava (Manihot esculenta)

Abstract Mosaic disease, caused by Cassava mosaic virus and transmitted by cassava whitefly, Bemisia tabaci (Gennadius; Hemiptera: Aleyrodidae), is the main threat to cassava production. Various genotypes with various degrees of resistance were employed to study the interactions between the whitefly and virus. These interactions included dispersal, feeding, fecundity, adult longevity and the life cycle. Virus acquisition in whiteflies altered their dispersal and settling behaviour. For the non‐viruliferous whiteflies that fed on resistant cassava genotypes, the speed of movement between leaves was higher compared to the viruliferous ones that fed on susceptible genotypes. Viruliferous whiteflies on susceptible genotypes fed more efficiently compared to non‐viruliferous ones on resistant genotypes, but fecundity was lowest in viruliferous whiteflies on resistant genotypes. The presence of virus in the system reduced adult longevity but extended the total life cycle. This study provides insights into how the virus modulates the behaviour and physiology/biology of the vector for enhanced transmissibility.

A BAC-guided haplotype assembly pipeline increases the resolution of the virus resistance locus CMD2 in cassava

BackgroundCassava is an important crop for food security in the tropics where its production is jeopardized by several viral diseases, including the cassava mosaic disease (CMD) which is endemic in Sub-Saharan Africa and the Indian subcontinent. Resistance to CMD is linked to a single dominant locus, namely CMD2. The cassava genome contains highly repetitive regions making the accurate assembly of a reference genome challenging.ResultsIn the present study, we generate BAC libraries of the CMD-susceptible cassava cultivar (cv.) 60444 and the CMD-resistant landrace TME3. We subsequently identify and sequence BACs belonging to the CMD2 region in both cultivars using high-accuracy long-read PacBio circular consensus sequencing (ccs) reads. We then sequence and assemble the complete genomes of cv. 60444 and TME3 using a combination of ONT ultra-long reads and optical mapping. Anchoring the assemblies on cassava genetic maps reveals discrepancies in our, as well as in previously released, CMD2 regions of the cv. 60444 and TME3 genomes. A BAC-guided approach to assess cassava genome assemblies significantly improves the synteny between the assembled CMD2 regions of cv. 60444 and TME3 and the CMD2 genetic maps. We then performed repeat-unmasked gene annotation on CMD2 assemblies and identify 81 stress resistance proteins present in the CMD2 region, among which 31 were previously not reported in publicly available CMD2 sequences.ConclusionsThe BAC-assessed approach improved CMD2 region accuracy and revealed new sequences linked to virus resistance, advancing our understanding of cassava mosaic disease resistance.

Trend Analysis of Four Cycles of National Cassava Mosaic Disease Surveys in Ghana

Cassava is an important staple crop in Africa. In Ghana, it is the number one root crop consumed by over 30 million people. It supports the livelihoods of farmers, stakeholders in the cassava value-chain and serves as raw material for industries. Despite its critical role, the crop faces substantial yield losses due to cassava mosaic disease (CMD) accounting for yield losses of more than 20% depending on the time of infection and the viral strain combinations. With the emergence of virulent strains of the CMV, routine surveys are necessary to ascertain the prevalence of CMD and their whitefly (Bemisia tabaci) vector in farmers’ fields. Field surveys were conducted in 2015, 2016/2017, 2019/2020 and 2022 using a harmonized sampling protocol developed by the Central and West Africa Virus Epidemiology for Roots and Tuber crops (WAVE) for food security. Diseased samples with varying symptoms collected were assayed using polymerase chain reaction (PCR) techniques. Whiteflies were collected from sampled plants within the top 5 uppermost leaves from five plants/field and then maintained in Eppendorf tubes containing 90% alcohol for laboratory analysis. In all, 1,113 fields were assessed as follows: 215, 178, 320 and 400 for 2015, 2016/17, 2019/20 and 2022 surveys respectively. African cassava mosaic virus (ACMV) and East African cassava mosaic virus (EACMV) strains were identified either singly or in mixed infection from samples collected with varying intensities during the periods of surveys. Whitefly counts from sampled plants showed similar trends of varied intensities. Disease severity and incidence also varied with the least disease incidence and severity being encountered in the 2022 survey suggesting success for the advocacy for the adoption of improved healthy planting materials.

Enhancing Farmers’ Capacity for Sustainable Management of Cassava Mosaic Disease in Côte d’Ivoire

Cassava Mosaic Disease (CMD) is a major constraint to cassava production in Côte d’Ivoire, causing significant yield and income losses for smallholder farmers. Despite its high prevalence, farmers’ knowledge and understanding of the disease remain limited. To address this issue, the Central and West African Virus Epidemiology (WAVE) Regional Center of Excellence provided capacity building for farmers in the major cassava growing regions. This study assesses the impact of the WAVE’s trainings and awareness campaigns on farmers’ knowledge of the disease and the management methods they adopted. Mixed socio-agronomic data were collected from 290 farmers, and CMD epidemiological parameters were assessed in 82 farms. Data were analysed using propensity score matching (PSM), followed by a Tobit regression model to assess the determinants and intensity of adoption of CMD management practices, using Stata. The results showed that trained farmers had a better understanding of CMD compared to untrained farmers. On average, trained farmers adopted 2.36 disease management practices (DMPs) compared to 1.55 DMPs for untrained farmers. Participation in WAVE’s training sessions and a sound knowledge of CMD positively influenced both the adoption and intensity of adoption of DMPs. However, there was no significant difference in CMD incidence between beneficiary areas (54.55%) and non-beneficiary areas (54.95%), likely due to the unavailability of disease-free planting material, inadequate agricultural practices, and high populations of whiteflies (Bemisia tabaci). This study shows the importance of awareness campaigns in the sustainable management of crop diseases in general and CMD in particular and suggests the need to train farmers on disease management and provide them with healthy planting materials.

Comparative analysis of pattern-triggered and effector-triggered immunity gene expression in susceptible and tolerant cassava genotypes following begomovirus infection.

South African cassava mosaic virus (SACMV) is one of several bipartite begomoviruses that cause cassava mosaic disease (CMD) which reduces the production yield of the cassava (Manihot esculenta Crantz) crop in many tropical and subtropical regions. SACMV DNA-A and DNA-B encoded-proteins act as virulence factors that aid in inducing different disease severity depending on the host response. Recent evidence suggests a mutual potentiation of cell membrane receptor-associated pattern-triggered immunity (PTI) and nucleotide leucine-rich repeat (NLR) effector-associated immunity (ETI) in plant immune responses. This study aimed to compare expression of SACMV virulence factors, and PTI/ETI, in SACMV-infected susceptible T200 and tolerant TME3 cultivars. Expression of SACMV virulence factors differed between SACMV-infected T200 and TME3 plants at 12, 32 and 67 days post infection (dpi). Notably, at the early stage of infection (12 dpi), expression in TME3 of AV1 and AC2 virulence factors were 10-fold and 30-fold down-regulated, respectively, compared to susceptible T200. At systemic infection (32 dpi) AV1 expression was also significantly lower (4-fold) in TME3 compared to T200. Expression of AC2 (that targets host innate immunity), while significantly lower in both T200 and TME3 at 32 dpi compared to 12 dpi, was also significantly down-regulated (16-fold) in TME3 compared to T200. TME3 recovers around 67 dpi and virus load decreases by 33%, while in T200, symptoms and high SACMV replication persist. Identification and comparison of induced PTI and ETI associated genes upon SACMV-infection in susceptible T200 and tolerant/recovery TME3 cassava genotypes was achieved by whole transcriptome sequencing (RNA-seq) and by reverse transcriptase quantitative PCR (RT-qPCR). Analyses revealed reduced expression of PTI-associated signalling and response genes during SACMV systemic/symptomatic infection (32 dpi) in cassava genotypes. In addition, hydrogen peroxide (H2O2) production, a PTI indicator, was significantly reduced in the symptomatic viral infection stage at 32 dpi. Concurrently at 32 dpi, transcription of ETI signalling and response genes as well as SA biosynthesis and response genes, were upregulated during SACMV systemic infection in TME3. These results indicate that SACMV targets PTI-associated genes during systemic infection at 32 dpi to subvert PTI-mediated antiviral immunity in cassava, which results in reduced induction of ROS production. Differential expression of specific NLR-associated genes also differed between susceptible and tolerant cultivars at 12, 32 and 67 dpi. SACMV virulence factors were shown to play a role in symptom severity in T200 and TME3.

Detection of Cassava Mosaic Disease and Assessment of Selected Agronomic Traits of Cassava (Manihot esculenta)

A study was conducted in Sierra Leone to identify cassava plants that are asymptomatic and symptomatic to cassava mosaic disease (CMD) and collect planting materials for field trial establishment; determine the prevalence of CMD caused by African cassava mosaic virus (ACMV) and East African cassava mosaic virus (EACMV) using the Nuru App and virus indexing techniques; and assess selected agronomic traits in cassava. A total of 80 cassava farms spanning four provinces (Southern, Eastern, Northern, and North-West) were surveyed in April 2022. Findings showed that the cassava variants of the experiment and locations significantly (p < 0.001) affected CMD incidence, severity, growth, and fresh storage root yield traits. The CMD incidence (87.0%) and whitefly abundance (144.8) were highest, and the CMD severity was moderate (4.0) for the plants derived from cuttings obtained from symptomatic Cocoa mother plants, while plants derived from cuttings of improved mother plants exhibited no visible symptoms of the disease and the lowest population (45.1) of whiteflies. The Nuru app is inefficient for phenotypically detecting CMD at 3 months after planting (MAP), while at 6, 9 and 12 MAP, the app efficiently detected the disease using a molecular analysis technique. Resistant, non-diseased plants derived from cuttings obtained from SLICASS 4 mother plants produced the highest fresh storage root yield (54.9 t ha−1). The highest storage root yield loss was recorded in the plants obtained from cuttings of symptomatic variety Cocoa mother plants harvested at Matotoka grassland ecology, Bombali District (90.2%), while those harvested from cuttings of asymptomatic variety Cocoa mother plants grown at the four test environments had a similar storage root yield loss ranging from 40.3 to 46.2%. Findings suggest the importance of genetic variability, environmental adaptation, utilization of diseased-free materials, and phytosanitation as disease management strategies for increased production. These findings provide important insights into the distribution, impact, and spread of CMD and whitefly abundance in the studied areas in Sierra Leone that could be exploited for cassava production, productivity, conservation, and population improvement.

Towards sustainable management of cassava mosaic disease: The impact of awareness campaigns in Benin.

Towards sustainable management of cassava mosaic disease: The impact of awareness campaigns in Benin.

Cassava Disease Detection Using Machine Learning Techniques

This study examines cassava disease detection using four convolutional neural network (CNN) models: ResNet50, InceptionV3, AlexNet, and VGG16. Cassava, a staple crop in Africa, is threatened by Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD). A dataset from the Lacuna Project, collected in Ugandan farmer fields, was used to train and evaluate these models, yielding accuracies of 90 percent, 88 percent, 85 percent, and 87 percent, respectively. A Flask web application was developed for practical deployment. This work builds on prior SVM and CNN approaches, offering a detailed comparison to enhance agricultural diagnostics for smallholder farmers.

Novel insights into Cassava mosaic disease using Caputo fractional derivative: modeling and analysis

In this paper, we present a novel fractional-order model for the transmission dynamics of Cassava Mosaic Disease (CMD), using the Caputo fractional derivative to account for memory effects intrinsic to disease processes. This innovative approach extends existing methodologies by capturing the long-term memory property of the disease, offering new insights into CMD dynamics. We rigorously establish the existence, uniqueness, positivity, and boundedness of the model's solution, ensuring their biological relevance. A significant contribution of this work is the derivation of the basic reproduction number R 0 using the next-generation matrix, followed by a detailed analysis of the local stability of equilibrium points through the Routh-Hurwitz criterion. Furthermore, a sensitivity analysis is conducted to identify critical parameters that most influence CMD transmissibility, providing essential guidance for targeted intervention strategies. Another key contribution is the implementation of numerical simulations via a generalized predictor-corrector approach, which vividly illustrates the influence of fractional-order derivatives on disease spread. The graphical results not only validate the theoretical results but also underscore the profound potential of fractional calculus in understanding CMD transmission and offer new perspectives and control strategies.

Enhancing Cassava Disease Detection: Leveraging Deep Convolutional Neural Networks and Data Augmentation for Accurate Diagnosis

Cassava, a widely cultivated staple food crop in the tropics, is frequently afflicted by various diseases that significantly decrease its yield. Cassava leaf disease diagnosis with four common cassava leaf diseases: cassava brown streak disease, cassava green mite, cassava bacterial blight, and cassava mosaic disease using state-of-the-art image classification techniques of deep CNN was implemented. In the proposed method data augmentation approaches have been applied to enlarge the dataset for better model performance. The methodology uses machine learning methods to surpass the conventional manual identification methods for recognizing affected leaf images, potentially increasing agricultural productivity through faster and more accurate disease detection that will lead to improved yield and better resource management in cassava cultivation. Evaluated the performance of various Deep Learning architectures, including LeNet, VGG-16, ResNet-50 and Efficient Net, as well as used various callback techniques. These include early stopping to prevent overfitting and model checkpointing, which saves the best-performing model, along with a learning rate reduction for fine-tuning these models and further optimizing their performance. The research, therefore, opens the possibilities of automated disease detection, greatly helping farmers in timely and accurate diagnosis for better management practices to ensure increased food security in cassava-growing regions, especially when mobile technology increases access to diagnostic tools and supports the field applicability of machine learning models. Further, deploying such models in the field has another advantage in embedding disease surveillance into the day-to-day activities of farmers, which again becomes an effective way to improve crop management strategies and responses in different agricultural contexts.

Removing recalcitrance to the micropropagation of five farmer-preferred cassava varieties in Côte d'Ivoire by supplementing culture medium with kinetin or thidiazuron.

In vitro micropropagation is a rapid method of multiplying healthy planting material to control Cassava mosaic disease (CMD), one of a major constraint to cassava production in Africa. However, some cassava varieties have a low propagation ratio under in vitro conditions. The main objective of this study was to improve the in vitro propagation rate of five difficult to grow, farmer-preferred cassava varieties using plant growth regulators. Microcuttings from in vitro plantlets of five recalcitrant cassava varieties (Agbablé 3, Ampong, Bayérè, Bocou 5, Olékanga) were evaluated for their capacity to rapidly regenerate plantlets. Time to root or leaf formation, number of nodes, number of roots, and the in vitro plantlet length were evaluated on nine culture media combinations. We found that among all the cassava varieties studied, the shortest times for leaf (4 to 7 days) or root (9 to 14 days) formation were recorded when the two types of MS media were supplemented with kinetin and thidiazuron as well as on the medium contain half-strength MS without these plant growth regulators. These two hormones evaluated were better for regeneration of leaves, nodes and elongation of in vitro plantlets with optimum concentration of 5 and 10 nM or thidiazuron, and 0.12 or 0.24 µM for KIN. A survival rate between 85-91% was recorded under tunnel conditions and the plantlets appeared to be morphologically normal. The information obtained during this study will be useful for mass multiplication programs of elite cassava varieties.

Identification of peptidome-based biomarkers of cassava mosaic disease resistance in different cassava varieties

Cassava, a major economic crop in Thailand, yielded over 3 million USD in exports in 2023. However, its production has been declining since 2021 due to cassava mosaic disease (CMD) outbreaks, which affect cassava plantations. CMD infections have recently increased due to the scarcity of healthy stems and CMD-resistant varieties, the latter being key to controlling its spread. Developing novel methods is critical for accelerating the cultivation of high-yield, CMD-resistant varieties. In this study, signature peptide patterns were determined using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and liquid chromatography-tandem MS (LC–MS/MS) to screen for CMD-resistant varieties. Peptide mass fingerprint (PMF) analyses revealed distinct peptide barcodes across 11 varieties, clearly delineating CMD-resistant and CMD-tolerant phenotypes. LC–MS/MS and orthogonal partial least squares-discriminant analysis (OPLS-DA) further demonstrated clear distinctions between the peptide profiles of different phenotypes. Heatmap and PMF analyses consistently revealed unique peptide patterns across the varieties. Volcano plot analysis identified seven upregulated peptides—TATTVAGS, PAAGGGGG, PNELLSYSE, SSIEEGGS, GGGVGGPL, NNGGGFSV, and GPGPAPAA—in CMD-resistant plants. These peptides were associated with proteins containing CONSTANS-like zinc finger, C2H2-type, GST N-terminal, Tubby-like F-box, nuclear-localized AT-hook motif, auxin response factor, and C2 domains. Altogether, this study identified peptidome-based biomarkers for screening CMD-resistant varieties; however, further validation using larger samples is necessary.

Enhancing Plant Resistance to Sri Lankan Cassava Mosaic Virus Using Salicylic Acid.

Background: Cassava mosaic disease (CMD), caused by the Sri Lankan cassava mosaic virus (SLCMV), significantly increases cassava yield losses in Thailand, with losses ranging from 30% to 80%, and is exacerbated by limited access to healthy planting materials. Methods: This study explored salicylic acid (SA) as a potential treatment for enhancing disease resistance in CMD infected cassava plants. SA was applied at 100 and 200 mg/mL, and its effects were evaluated using quantitative real-time polymerase chain reaction (qPCR) and reverse transcription qPCR (RT-qPCR) to measure viral loads and the expression levels of resistance genes. Results: Although SA treatment did not considerably affect disease severity, foliar CMD symptoms visibly decreased, particularly with 200 mg/mL SA, which also reduced SLCMV particle counts at 1- and 2-weeks post-treatment. SA upregulated the expression of pathogenesis-related proteins (PRs), including HSP90.9, WRKY59, SRS1, and PR9e. Additionally, SA enhanced the regulation of secondary metabolite pathways involving L-serine within the glycine, serine, and threonine metabolism, as well as the phenylpropanoid biosynthesis pathways. Conclusions: These findings collectively indicate that SA enhances resistance through the systemic acquired resistance (SAR) pathway and can serve as a potential strategy for the management of CMD, particularly in regions where healthy cassava planting materials are scarce. The study highlights the efficacy of SA in reducing viral particles, inducing the immune response, and providing a promising approach for controlling CMD.

Roles of WRKY Transcription Factors in Response to Sri Lankan Cassava Mosaic Virus Infection in Susceptible and Tolerant Cassava Cultivars.

Cassava mosaic disease (CMD) is caused by viruses such as Sri Lankan cassava mosaic virus (SLCMV). It poses a significant threat to the cassava (Manihot esculenta) yield in Southeast Asia. Here, we investigated the expression of WRKY transcription factors (TFs) in SLCMV-infected cassava cultivars KU 50 (tolerant) and R 11 (susceptible) at 21, 32, and 67 days post-inoculation (dpi), representing the early, middle/recovery, and late infection stages, respectively. The 34 identified WRKYs were classified into the following six groups based on the functions of their homologs in the model plant Arabidopsis thaliana (AtWRKYs): plant defense; plant development; hormone signaling (abscisic, salicylic, and jasmonic acid); reactive oxygen species production; basal immune mechanisms; and other related hormones, metabolites, and abiotic stress responses. Regarding the protein interactions of the identified WRKYs, based on the interactions of their homologs (AtWRKYs), WRKYs increased reactive oxygen species production, leading to salicylic acid accumulation and systemic acquired resistance (SAR) against SLCMV. Additionally, some WRKYs were involved in defense-related mitogen-activated protein kinase signaling and abiotic stress responses. Furthermore, crosstalk among WRKYs reflected the robustly restricted viral multiplication in the tolerant cultivar, contributing to CMD recovery. This study highlights the crucial roles of WRKYs in transcriptional reprogramming, innate immunity, and responses to geminivirus infections in cassava, providing valuable insights to enhance disease resistance in cassava and, potentially, other crops.

Automated Diagnosis of Cassava Mosaic Disease Through Advanced Deep Learning Techniques

Cassava Mosaic Disease (CMD) is a major trouble to cassava crops, causing significant losses in agrarian product worldwide. Beforehand and accurate discovery of CMD is pivotal to alleviate its impact. Traditional styles of complaint discovery, similar as homemade examination and lab- grounded diagnostics, are time- consuming, precious, and frequently unreliable In this exploration, we propose an innovative approach to CMD discovery using a Deep intermittent Neural Network (DRNN) fashion, using image data from cassava shops. The DRNN model is designed to dissect factory images and descry symptoms of CMD with high perfection, offering a more effective and scalable result than conventional styles. The primary ideal of this study is to enhance the discovery delicacy of CMD using deep literacy ways, specifically DRNN, which combines the power of convolutional neural networks (CNN) with intermittent layers for bettered point birth and pattern recognition in factory images. We use a large data set of cassava factory images, including both healthy shops and those affected by CMD, to train and validate the model. Data addition ways were applied to ameliorate the model’s conception and robustness. Our results show that the DRNN model achieves a discovery delicacy of over 90, outperforming traditional styles and furnishing real- time individual capabilities. This approach not only improves the effectiveness of complaint discovery but also empowers growers with a cost-effective tool for covering factory health. The proposed system can be further expanded to descry other factory conditions, offering a protean result for agrarian health operation. This exploration contributes to the field of agrarian technology by demonstrating the eventuality of DRNNs in factory complaint discovery and offers a promising direction for unborn advancements in automated crop operation systems. Highlights Preface of DRNN The paper explores the operation of Deep intermittent Neural Networks (DRNN) for effective complaint discovery in cassava shops. ⮚ CMD Impact Emphasizes the significant profitable and agrarian impact of Cassava Mosaic Disease (CMD) on cassava product encyclopaedically. ⮚ Early Discovery significance Highlights the need for beforehand, accurate discovery of CMD to alleviate crop losses and ameliorate yield. ⮚ Deep literacy operation Demonstrates how deep literacy models, especially DRNN, can be used to enhance factory complaint discovery from images. ⮚ Data Augmentation Utilizes data addition ways to ameliorate the conception capability of the DRNN model. ⮚ Cassava Image Dataset The study uses a large and different dataset of cassava factory images, including both healthy and CMD affected shops. ⮚ Model Architecture Details the DRNN armature, combining convolutional and intermittent layers to effectively capture temporal and spatial features in factory images. ⮚ Delicacy Achievement The proposed DRNN model achieves over 90 delicacies in detecting CMD, outperforming traditional styles. ⮚ Relative Analysis The paper compares the DRNN model’s performance with other common deep literacy models, demonstrating its superior performance. ⮚ Real- Time opinion DRNN provides real- time individual capabilities for CMD discovery, offering immediate perceptivity for growers. ⮚ Cost- Effectiveness The exploration shows that the proposed system is cost-effective, making it accessible for use in small- scale and large- scale husbandry. ⮚ Scalable result the system can be fluently gauged to descry other factory conditions, furnishing a protean tool for husbandry. ⮚ Model Robustness The model’s robustness is bettered by applying colourful data preprocessing ways similar as normalization and image resizing. ⮚ Impact on Agricultural Practices The study highlights how automated CMD discovery can transfigure agrarian practices and decision-timber. ⮚ Unborn exploration Directions The paper suggests farther exploration into integrating DRNN with other AI ways for indeed more accurate complaint discovery and vaticination.

Improvement of Nanopore sequencing provides access to high quality genomic data for multi-component CRESS-DNA plant viruses

BackgroundFaced with the recrudescence of viral CRESS-DNA plant diseases, the availability of efficient and cost-effective tools for routine diagnosis and genomic characterisation is vital. As these viruses possess circular single-strand DNA genomes, they have been routinely characterised using rolling circle amplification (RCA) coupled with Sanger sequencing. However, while providing the basis of our knowledge of the diverse CRESS-DNA viruses, this approach is laboratory-intensive, time-consuming and ultimately ineffective faced with co-infection or viruses with multiple genomic components, two common characteristics of these viruses. Whereas alternatives have proved effective in some applications, there is a strong need for next-generation sequencing methods suitable for small-scale projects that can routinely produce high quality sequences comparable to the gold standard Sanger sequencing.ResultsHere, we present an RCA sequencing diagnostic technique using the latest Oxford Nanopore Technology flongle flow cells. Originally, using the tandem-repeat nature of RCA products, we were able to improve the quality of each viral read and assemble high-quality genomic components. The effectiveness of the method was demonstrated on two plant samples, one infected with the bipartite begomovirus African cassava mosaic virus (ACMV) and the other infected with the nanovirus faba bean necrotic stunt virus (FBNSV), a virus with eight genomic segments. This method allow us to recover all genomic components of both viruses. The assembled genomes of ACMV and FBNSV shared 100% nucleotide identity with those obtained with Sanger sequencing. Additionally, our experiments demonstrated that for similar-sized components, the number of reads was proportional to the segment frequencies measured using qPCR.ConclusionIn this study, we demonstrated an accessible and effective Nanopore-based method for high-quality genomic characterisation of CRESS-DNA viruses, comparable to Sanger sequencing. Face with of increasing challenges posed by viral CRESS-DNA plant diseases, integrating this approach into routine workflows could pave the way for more proactive responses to viral epidemics.

Identifying cassava production constraints, farmers preferences, and cassava mosaic disease perceptions in Togo: insights for a participatory breeding approach

BackgroundCassava is a crucial food security crop in Togo and the most significant root crop in terms of area under cultivation and production volume. However, its production is predominantly carried out by subsistence farmers using low-yielding landraces. Several constraints impede cassava production, threatening its sustainability in the country. The low adoption of improved varieties developed by the International Institute of Tropical Agriculture (IITA) underscores the need for a participatory approach to research and development. This study aimed to identify the cassava varieties grown, major production constraints, farmers' trait preferences, and perceptions of Cassava Mosaic Disease (CMD) through Participatory Rural Appraisal (PRA).MethodsThe study employed a multistage random sampling procedure to select regions, districts, and villages based on cassava production levels. The survey involved 83 men and 57 women in group interviews and 600 farmers in individual interviews: 200 in the forest–savanna transition, 180 in the rainforest, 120 in the wet savanna, and 100 in the dry savanna. Content analysis was used for qualitative data, and quantitative data were analyzed using descriptive statistics and comparative analyses, including Chi-square tests to assess differences in perceptions and preferences.ResultsThe PRA revealed key constraints to cassava production, including inadequate capital, CMD, post-harvest physiological deterioration (PPD), and the non-availability of clean planting materials. Traits such as high yield, pest and disease resistance, early maturity, high dry matter content, delayed root deterioration, poundability, and taste were highly valued by farmers. CMD was identified as a significant disease, causing yield losses, with various causes and management practices reported. The study highlighted the necessity for a sustainable cassava seed system, as farmers pointed out the lack of improved varieties and clean planting materials.ConclusionThis study provides essential insights into cassava farming practices, production constraints, and farmers' preferred traits, laying the groundwork for a participatory breeding program in Togo. Addressing low-yielding varieties and diseases, particularly CMD, is critical for enhancing cassava production and ensuring food security.

Interactive Effect of Irrigation Supplementation, Compost‐Fertilisation and Resistant Cassava Varieties on the Control and Management of CMD and CBSD, Incidences and Severity Through Morphological Detection Technique

ABSTRACTThe field experimental study was conducted to investigate or determine and analyse the interactive effect of better agronomical practices such as cropping system, irrigation, compost‐fertilisation, planting dates and resistant varieties on the cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) disease presence, incidences and severity through morphological detection technique. The qualitative and quantitative primary data were collected and analysed, through Generalised Model Analysis of Variance (ANOVA), linear regression analysis, GENSTAT and JMP Pro software from this research field experiment study which were laid out in a RCBD design with three blocks and four replications in different location sites. The study took place in Mara and Coastal regions, at Nyasirori Butiama district and Nyegina and Busungu‐Majita Musoma rural district and Msoga Bagamoyo Coast region between October 2021 and August 2023. The results findings show that the disease incidence and levels of severity varied or differed significantly among the four location sites and between the two provinces or zones, Coastal and Mara of the Lake zone, at p < 0.001 level of significance. However, the findings show that there were greater levels of disease incidence and severity in Mara region means (1.9199) particularly Nyegina and Busungu‐Majita villages (2.034632) and (2.0002706), respectively, than Msoga Coast region and Nyasirori Mara region, which both recorded low levels of disease incidence and severity of (1.8509) and (1.65536), respectively. But likewise, the higher mean for disease severity scale score showed to decline progressively from (4.2 ± 0.24) Nyegina, (3.2 ± 0.14) Busungu‐Majita, (2.5 ± 0.2) Msoga and (2.0 ± 0.21) Nyasirori having the lowest level of severity. Moreover, the results also found that there was significant influence at p < 0.001 level of significance with agronomic practice treatment, particularly the application of compost FYM + solely or in combination with irrigation supplementation on reducing and controlling the disease incidence and severity, similarly with growth stage and varietal influence. However, conversely again, the result findings had shown significant reduction and management of the disease incidence and severity levels following treatment application of agronomic practices with FYM and Fertilisation (1.817703), Monocropping + FYM + Irrigation (1.8238636) and Irrigation + FYM + Crop rotation (1.7921402) treatments. Similarly, with the varieties influence, particularly with Mkuranga 1, Kipusa, Chereko and Kiroba, had shown the lowest means (1.285871), (1.474808), (1.711939) and (1.819277), respectively, among the varieties tested. However, the best results for disease prevention, management and control, and thus the recommended application rates of compost FYM + Fertiliser materials were found to be lower and/or ranging between 40 and 60 kg per row equivalent to 20.0–32.0 t ha−1 application rates and 30 kg NPK and 10 kg CAN ha−1 depending on soil type (i.e., too poor and too sandy or sandy–sandy loam) and fertility status of the soil, since even lower rates still have shown significant control. Finally, this will have a tangible benefit of reducing the diseases pandemic to farmers and cassava crop stakeholders, agriculture industry sector on the increased crop productivity performance, yield and more importantly the government policy on increased use and application of compost FYM and fertilisers and a very low cost of production countrywide.

Early prediction of cassava mosaic disease onset based on remote sensing and climatic data

Early prediction of cassava mosaic disease onset based on remote sensing and climatic data

Optimal control applied to a stage-structured cassava mosaic disease model with vector feeding behavior

Optimal control applied to a stage-structured cassava mosaic disease model with vector feeding behavior