This is the report from a BSPP Lockdown Bursary.
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One of the world’s most important food crops is cassava (Manihot esculenta Cranz.) which belongs to the family Euphorbiaceae. Cassava roots and leaves serve as an essential source of calories and income throughout the tropics. Many people in Africa, Asia and Latin America depend on the cassava crop for their food and income.
Cassava is a major source of carbohydrates for millions of people in several regions, particularly in developing countries. The cassava crop plays a vital role in reducing poverty and rural exodus because the use of technology required is minimal. Cassava serves as raw material for more than 80 industrial products worldwide. It represents a delicacy, enabling the processing of many culturally appreciated recipes. Cassava is imperatively needed for human consumption, livestock feed and in industries. Since independence, the production has tripled with an average production of 2,109,040 milion tons per year still, the demand is hardly met due to diseases such as mosaic, root rot, fungal rot and pests like whiteflies which impede the yield. Despite the relative progress observed in world annual yield of cassava, its cultivation is faced with several pests and diseases such as the fungal root rot disease of cassava, which affects cassava roots found in humid or poorly drained soil. Fungal pathogens are the main pathogens associated with CRRD in Africa. Examples include Fusarium spp. causing dry rot (F. solani, F. oxysporum, and F. verticillioides); Phytophthora spp. (P. nicotianae and P. drechsleri), Pythium scleroteichum, associated with soft rot, and Neoscytalidium hyalinum and Lasiodiplodia spp. causing black rot. Knowledge of the diversity and geographical distribution of root rot pathogens may be useful to breeders targeting root rot resistance.
Postharvest food loss (PHL) is defined as measurable qualitative and quantitative food loss along the supply chain, starting at the time of harvest to consumption or other end uses. Postharvest activities include harvesting, handling, storage, processing, packaging, transportation, and marketing. Losses of produce are a major challenge in the post-harvest chain. Over the past 40 years, 40-50% of food crops produced in developing countries is lost before they can be consumed mainly because of high rates of bruising water loss and subsequent decay during posts harvest handling. The magnitude of these losses and their impact on farm income varies from place to place. The importance of postharvest management lays in its capacity to meet the food requirements of growing populations most especially that of Sub Saharan Africa by reducing losses and increasing the production of nutritive food items.
In Cameroon, roots, and tubers account for 70% of the total cultivated area and 46% of food crop production, with a total cassava production estimated at 2,882,734 tons in 2009. 80% of urban households in Cameroon consume cassava products on a daily basis, and about 90% of small-scale producers market at least a small part of the cassava they produce.
In Cameroon, about 36% of farmers classify cassava root rot as the second cause of reduced yields in the cassava sector. However, proper identification of pathogenic fungi associated with this disease is yet to be done. Also, food production equally is critical for ensuring global food security, but this is not sufficient if proper management technics of food crops after harvest are not employed. In addition, agriculture is constantly being challenged by climatic variability which has had a great impact on crop produce in terms of quality and quantity of crops produced. Post- harvest food loss in Africa represents a multi-faceted challenge that reduces the income of approximately 470 million farmers and other value chain actors by as much as 15%. About a third of food produced in Cameroon is wasted due to poor postharvest management of cassava exposing the roots to fungal attack leading to fungal diseases. This postharvest food loss greatly has devastating consequences on farmer’s food security, nutrition, health, and livelihoods.
The objectives of this study were: i) to study the intensity (incidence and severity) of fungal diseases of cassava root in Bamenda, ii) to identify the different fungal species associated with cassava root in Bamenda, iii) to investigate the effect of soil physiochemical property on fungal disease incidence and severity of cassava in Bamenda, iv) to evaluate the post-harvest losses associated with fungal diseases of Cassava in Bamenda, v) to carryout pathogenicity test on the pathogens responsible for fungal diseases on Cassava.
Field surveys were conducted in Bamenda between October to December 2021; to assess the variability of fungal diseases associated with cassava root rot in farmers’ farms in Bamenda. Between February and March, a total of 20 farms were assessed, 10 each in Nkwen and Bambili. An opportunistic sampling method was used to select the farms. Only secondary cassava fields (more than 6 months) were sampled. From each farm, 50 cassava plants were sampled according to the method of Nyaka et al. (2015) with slide modification. At each farm, data was taken on location including coordinates using GPS from which a Geographic Information System (GIS) map was generated from the cassava farms assessed. The crop age was taken from the farmers and the size of the field was determined by visual counting; whereby the individual cassava stems were counted. The cassava variety (whether local or improved) were also noted. The number of cassava stems showing symptoms of infection were recorded per farm from which disease incidence was calculated. Wilted and/or dead plants showing ant/termite damage were excluded in the estimation of disease incidence. Percent incidence was also calculated. Disease severity was assessed using the 1 to 5 severity scale of the International Institute of Tropical Agriculture (IITA) where 1 represents no symptoms and 5 the most severe disease symptoms including severe leaf deformation and general plant stunting or wilting.
To identify the fungal pathogen associated with disease, a random sampling method was conducted. The cassava leaves were assessed for the presence of external symptoms such as leaf browning, discoloration of the lower parts, and generalised wilting, which are most often indicative of root infections. Five cassava leaves were collected per farm from the cassava plants at regions of no disease symptoms, low disease symptom, moderate disease symptom and high disease symptom. These samples were transported to the Life Science Laboratory, University of Buea, for isolation and identification of different fungal pathogens since the symptoms produced by these pathogens are sometimes difficult to distinguish from each other in the field.
To isolate and purify the fungi, isolation was done on Potato Dextrose Agar (PDA) enriched with gentamicin and penicillin to avoid bacterial growth. Infected cassava leaves were washed under running tap water. Using a razor blade, about 5 mm portions of infected leaf were cut out, rinsed serially in 10 0C bleached distilled water, 70 % alcohol and sterile distilled water in 3 separate beakers. The leaf portions were removed and dried with sterile blotting-paper. This was done under aseptic laboratory working conditions. With forceps, 2 sterilised leaf portions were cultured per petri dish and incubated at room temperature. It was observed daily for 5 days. Fungal colonies that emerged from the infected leaf portions were subculture individually on new PDA culture medium. The action was repeated until pure cultures were obtained. The mycelia were harvested after 5 days and stored for molecular analysis. Fungi identification was done with reference to the key to Barnett and Hunter (1972). DNA was extracted using a sorbitol-CTAB DNA extraction method.
To characterise the soil, soil sampling was carried out between February and March 2022. Soil from the research cassava farms was collected using a soil auger from 20 farms (10 from Nkwen and 10 from Bambili). Soil collection per farm was done at the rhizosphere (soil surrounding cassava roots) from areas of no disease incidence, low disease incidence, moderate disease incidence and high disease incidence. From each level of disease incidence in each farm, 10 soil samples were collected from different points and bulked. Each soil sample was replicated twice and sampling point geo-referenced using GPS navigation. The samples were and transported to the University of Dschang, Soil Science Laboratory for analysis.
Soil samples were transferred to a plastic tray and air dried for days without placing under direct sunlight. Large clods were broken down and plant residues removed. After drying, soil was weighed and sieved through a 2 mm sieve. Gravel and rock fragments were discarded. The homogenized soil was stored in plastic refrigerator bottles and labelled.
The soil samples were characterised for both physical and chemical characteristics. Soil pH (both PH water and PH potassium chloride) was determined through the potentiometric method. Soil clay, silt and sand percentage will be determined using the pipette method. Soil organic concentration and organic matter was determined using the Walkley –Black procedure at 1250C. Percentage of soil nitrogen was determined by the Kjeldahl method. Available phosphorus (Olsen-P) was determined by the method of Olsen et al. (1954). Available calcium and magnesium was determined using the flame atomic absorption spectrophotometry (AAS) while potassium and magnesium was determined using the flame emission spectrophotometry (FES). Cation exchange capacity was gotten using the mechanical extractor method.
This research findings showed that; on average, the severity of cassava disease in Bamenda according to disease severity scale is 2.55 (approx. 3) which represents ‘less disease severity. Also, cassava disease symptoms in Bambili (2.7) was more severe than in Nkwen (2.4) (Table 1).
Table 1: Disease Incidence and Severity Measurements
|Locality||Cassava Farm (F)||Disease Incidence||Percentage Incidence (%)||Disease Severity Scale||Latitude||Longitude||Altitude|
Also, the identification of fungi carried out on the basis of colony characteristics on culture media indicated that, the fungal isolates showed different growth diameter, colony form, and colony colour on media (Table 2). From molecular identification, 25 specimens were amplified using both ITS and TEF gene regions. The length of the amplified product was between 600-700 bp. From the sequences, 10 species of pathogenic fungi belonging to nine families were identified with the help of NCBI using BLAST. These species and their respective families include: Clonostachys rosea (Bionectriaceae), Cladosporium oxysporium (Davidiellaceae), Epicoccum sorghinum (Didymellaceae), Geotrichum Candidum (Dipodascaceae), Trichoderma harzianum (Hypocreaceae), Graphium penicillioides (Microascaceae), Fusarium oxysporium (Nectriaceae), Curvularia parasadii (Pleosporaceae), Aspergillus sclerotiorum (Trichocomaceae) and Penicillium Citrinum (Trichocomaceae). The results obtained had high values for maximum identity and query coverage (Table 3.)
Table 2: Growth Parameters of Fungal Isolates
|No||Sample Code(S)||Colony Diameter||Colony|
|Surface Colony Colour||Reverse Colony Colour||Suspected |
Table 3: Molecular Identification of Fungal Pathogens
|Sample ID||Scientific Name||Maximum Score||Total Score||Query Coverage (%)||E. value||Percentage |
|Acc Length||Accession |
|C1||Trichoderma harzianum||763||763||95||0.0||99.29||1247||ON010777.1||Wang et al., 2022|
|C2||Geotrichum Candidum||730||730||91||0.0||98.77||411||MK397511.1||Magray et al., 2019|
|C3||Geotrichum Candidum||769||769||96||0.0||98.83||438||MK397512.1||Magray et al., 2019|
|C4||Geotrichum Candidum||750||750||97||0.0||98.54||635||MK397513.1||Magray et al., 2019|
|C7||Epicoccum sorghinum||702||702||97||0.0||95.98||963||OK236518.1||Khana et al.,2021|
|C10||Fusarium oxysporium||726||726||56||0.6||97.36||6642||MX03118685.1||Ma et ., 2022|
|C12||Clonostachys rosea||608||608||54||4e-169||91.89||963||LT220770.1||Sharma and Marques, 2016|
|C13||Penicillium Citrinum||715||715||97||0.0||90.15||808||MH213252.1||Tischner et al., 2018|
|C14||Trichoderma ganodermatigenum||750||750||97||0.0||98.70||1294||ON567198.1||An et al., 2022|
|C15||Trichoderma breve||756||756||42||0.0||99.54||1102||MN605884.1||Gu et al.,2020|
|C16||Curvularia parasadii||741||741||93||0.0||99.51||861||KM230408.1||Manamgoda et al., 2014|
|C17||Fusarium oxysporium||763||763||94||0.0||99.52||824||KT323846.1||Ortiz et al., 2017|
|C18||Trichoderma harzianum||533||533||63||1e-152||87.40||1247||ON010777.1||Wang et al., 2022|
|C19||Fusarium oxysporium||749||749||94||0.0||99.8||6642||XMO31186851.1||Ma et al., 2011|
|C20||Graphium penicillioides||568||568||82||4e-157||88.80||954||LT615302.1||Sharma and Marque, 2016|
|C21||Trichoderma breve||717||717||42||0.0||97.64||632||MN389578.1||Hansen, 2019|
|C22||Fusarium graminearum||771||771||99||0.0||99.5||90257552||LT222054.1||King , 2016|
|C24||Fusarium oxysporium||769||769||96||0.0||100||1755||MK968885.1||Meena, 2019|
|C25||Trichoderma harzianum||621||621||93||3e-173||90.85||1247||MW267420.1||Xu et al., 2020|
|C30||Fusarium oxysporium||752||752||93||0.0||99.04||650||XM031186852.1||Ma et al., 2022|
|C31||Fusarium oxysporium||597||597||84||4e-166||98.00||907||KU938992.1||Wang et al., 2016|
|C36||Aspergillus sclerotiorum||553||553||42||2e-152||89.16||570||MH029905.1||Pchelin, 2018|
Table 4 shows the variation in physicochemical properties of soils from 20 cassava farms in Bamenda. In terms of physical properties, clay, silt and sand show same values (27%, 20.55 and 52.5% respectively) within the different research farms indicating that, the soil physical properties were the same across the farms. With respect to soil chemical properties, there was a great variation across the farms.
Table 4. Physicochemical Properties of Different Soil Samples from 20 Cassava Farms in Bamenda
|Base sat uration%|
This research has helped me to developed good understanding of fungal isolation and identification techniques using both morphological and molecular techniques. Being my first research on soil analysis with respect to diseases intensity is a great experience. In addition, I was able to interact with famers and other scientist in my field whom we had a common interest of meeting cassava demands through adequate post-harvest handling, food security and crop protection.
Objectives to evaluate the post-harvest losses associated with fungal diseases of Cassava in Bamenda and to carry out a pathogenicity test on the pathogens responsible for fungal diseases on Cassava in Bamenda respectively, are to be executed.
To evaluate the postharvest losses associated with fungal diseases of cassava, field surveys will be undertaken in two phases; the 1st phase will be based on field observation, discussions, and snap shots and will run from December 2022 to January 2023. From this part, the study site will be demarcated with focus on the various postharvest management strategies and the challenges faced by farmers in preserving cassava roots within Bamenda. The 2nd phase will be the administration of semi-structured questionnaire. A total of 500 questionnaires will be designed and administered to cassava famers in the sampling site (Nkwen and Bambili) to acquire information on postharvest losses associated with fungal diseases, postharvest management techniques, the various processes involved and the challenges they faced by farmers. A random sampling of households will be done to ensure that, each farmer has a chance to be selected without bias.
For pathogenicity test, a greenhouse will be constructed at the University of Bamenda Campus for this experiment. The mycelial plug and conidia from the fungal isolates will be used to inject the plants in the greenhouse. Initial symptoms will be observed on wounded and unwounded leaves at three days interval after inoculation by mycelia plug. The diameter of the disease symptoms that appear will be taken. The disease symptoms will be compared to those seen on the leaves inoculated with mycelial plugs. Koch’s postulates will be followed through the fungi re-isolated from symptomatic leaf tissue and then grown back on PDA. The re-isolated fungi will then be identified. This objective will be executed from July – August 2023.
I am grateful to my supervisors; Prof TONJOCK Rosemary KINGE and Prof MBONG Grace for their constant corrections and support towards this project. Also, I am very grateful to the BSPP for their financial support towards the realisation of this project.
AZINUE CLEMENTINE LEM
The University of Bamenda, Cameroon