Browsing by Author "Dzidzienyo, D."
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Item Aflatoxin contamination of groundnut (Arachis hypogaea L.): Predisposing factors and management interventions(Food Control, 2019-04) Asare Bediako, K.; Ofori, K.; Offei, S.K.; Dzidzienyo, D.; Asibuo, J.Y.; Adu Amoah, R.Groundnut (Arachis hypogaea L.) is one of the most important oilseed crops in world agricultural trade. It is considered an important crop by virtue of its contribution to satisfying the protein needs of many households who cannot afford animal protein. Production and consumption of groundnuts are hampered among others, by Aspergillus flavus and Aspergillus parasiticus infection which subsequently contaminate groundnuts with aflatoxins. Aflatoxins are associated with acute and chronic toxicities in humans and animals causing induction of tumor, liver damage, liver cirrhosis, and carcinogenic, estrogenic, teratogenic, and immunosuppressive effects. Contaminated food crops expose millions of people to high risk of chronic aflatoxin exposure. Aflatoxin contamination can occur in the field before harvest, and after harvest during curing, storage and transportation. The major factors influencing A. flavus and A. parasiticus infection in groundnuts before harvest are insect damage to the developing seed/pod, drought and high soil temperatures. After harvest, environmental conditions such as high humidity and high temperatures promote fungal infection and aflatoxin accumulation. Agronomic practices such as crop rotation, use of resistant varieties, insect control, timely planting and harvesting, weed control, adequate fertilization and late season irrigation can reduce pre-harvest aflatoxin production. Additionally, atoxigenic fungi can be applied in the field to competitively displace toxigenic fungi to reduce the population of toxigenic fungi in the soil. Post-harvest aflatoxin contamination of groundnuts can be minimized by rapid and proper drying following harvesting, proper transportation and packaging, sorting and post-harvest insect control. Sourcing information from different research and review articles, and book chapters, this paper provides extensive review on the predisposing factors and management of groundnut aflatoxin contamination before and after harvest.Item Genetic characterization of cassava (Manihot esculenta Crantz) genotypes using agro-morphological and single nucleotide polymorphism markers(Physiol Mol Biol Plants, 2019-12-23) Karim, K.Y.; Ifie, B.; Dzidzienyo, D.; Danquah, E.Y.; Blay, E.T.; Whyte, J.B.A.; Kulakow, P.; Rabbi, I.; Parkes, E.; Omoigui, L.; Norman, P.E.; Iluebbey, P.Dearth of information on extent of genetic variability in cassava limits the genetic improvement of cassava genotypes in Sierra Leone. The aim of this study was to assess the genetic diversity and relationships within 102 cassava genotypes using agro-morphological and single nucleotide polymorphism markers. Morphological classification based on qualitative traits categorized the germplasm into five different groups, whereas the quantitative trait set had four groups. The SNP markers classified the germplasm into three main cluster groups. A total of seven principal components (PCs) in the qualitative and four PCs in the quantitative trait sets accounted for 79.03% and 72.30% of the total genetic variation, respectively. Significant and positive correlations were observed between average yield per plant and harvest index (r = 0.76***), number of storage roots per plant and harvest index (r = 0.33*), height at first branching and harvest index (0.26*), number of storage roots per plant and average yield per plant (r = 0.58*), height at first branching and average yield per plant (r = 0.24*), length of leaf lobe and petiole length (r = 0.38*), number of leaf lobe and petiole length (r = 0.31*), width of leaf lobe and length of leaf lobe (r = 0.36*), number of leaf lobe and length of leaf lobe (r = 0.43*), starch content and dry matter contentItem Genotype‑by‑environment interaction and stability of root mealiness and other organoleptic properties of boiled cassava roots(Scientific Reports, 2022) Uchendu, K.; Njoku, D.N.; Ikeogu, U.N.; Dzidzienyo, D.; Tongoona, P.; Ofei, S.; Egesi, C.Genetic enhancement of cassava aimed at improving cooking and eating quality traits is a major goal for cassava breeders to address the demand for varieties that are desirable for the fresh consumption market segment. Adoption of such cassava genotypes by consumers will largely rely not only on their agronomic performance, but also on end-user culinary qualities such as root mealiness. The study aimed to examine genotype× environment interaction (GEI) efects for root mealiness and other culinary qualities in 150 cassava genotypes and detect genotypes combining stable performance with desirable mealiness values across environments using GGE biplot analysis. Experiments were conducted using an alpha-lattice design with three replications for two years in three locations in Nigeria. The analysis of variance revealed a signifcant infuence of genotype, environment, and GEI on the performance of genotypes. Mealiness scores showed no signifcant relationship with frmness values of boiled roots assessed by a penetration test, implying that large-scale rapid and accurate phenotyping of mealiness of boiled cassava roots remains a major limitation for the efective development of varieties with adequate mealiness, a good quality trait for direct consumption (boil-and-eat) as well as for pounding into ‘fufu’. The moderate broad-sense heritability estimate and relatively high genetic advance observed for root mealiness suggest that signifcant genetic gains can be achieved in a future hybridization program. The genotype main efects plus genotype× environment interaction (GGE) biplot analysis showed that the diferent test environments discriminated among the genotypes. Genotypes G80 (NR100265) and G120 (NR110512) emerged as the best performers for root mealiness in Umudike, whereas G13 (B1-50) and the check, G128 (TMEB693) performed best in Igbariam and Otobi. Based on the results of this study, fve genotypes, G13 (B1-50), G34 (COB6-4), G46 (NR010161), the check, G128 (TMEB693), and G112 (NR110376), which were found to combine stability with desirable mealiness values, were the most suitable candidates to recommend for use as parents to improve existing cassava germplasm for root mealinessItem Identification of sources of resistance to Alectra vogelii in cowpea [Vigna unguiculata (L.) Walp.] germplasm from Burkina Faso(Euphytica, 2018-11) Dieni, Z.; De La Salle Tignegre, J.B.; Tongoona, P.; Dzidzienyo, D.; Asante, I.K.; Ofori, K.In Burkina Faso, cowpea serves as both a food and cash crop generating significant income for stakeholders. However, cowpea production is seriously hampered by several constraints among which is the parasitic weed Alectra vogelii. There is therefore an urgent need to identify Alectra-resistant genotypes, possessing farmers’ desired traits, in cowpea germplasm in Burkina Faso. Cowpea genotypes were screened under artificial (screen house) and natural (field) A. vogelii infestation for their reaction to the weed. For the screen house experiment the data were collected on parameters such as days to cowpea flowering and maturity, days to Alectra shoots emergence, the severity of Alectra attack. Cowpea grain yield, Alectra shoots count and severity were scored from the field experiment. These data were subjected to analysis of variances in SAS 9.4. Correlation analysis was also run to determine the association between Alectra infestation parameters and cowpea yield. Twenty Genotypes including KVx414-22-2, KVx165-14-1, Komcalle, IT99K-573-2-1, IT98K-205-8, IT86D10-10, IT93K-693-2 and B301 were identified to be resistant to A. vogelii. Variety Komcalle, IT99K-573-2-1 and IT98K-205-8 are improved and farmers’ adopted varieties. Therefore, these varieties can be recommended for Alectra-infested areas. The other genotypes showing resistance are potential donor parents for resistance to A. vogelii. © 2018, Springer Nature B.V.Item Inheritance of Striga hermonthica adaptive traits in an early‐maturing white maize inbred line containing resistance genes from Zea diploperennis(Plant Breeding, 2019-04) Akaogu, I.C.; Badu-Apraku, B.; Tongoona, P.; Ceballos, H.; Gracen, V.; Offei, S.K.; Dzidzienyo, D.Striga hermonthica can cause as high as 100% yield loss in maize depending on soil fertility level, type of genotype, severity of infestation and climatic conditions. Understanding the mode of inheritance of Striga resistance in maize is crucial for introgression of resistance genes into tropical germplasm and deployment of resistant varieties. This study examined the mode of inheritance of resistance to Striga in early‐maturing inbred line, TZdEI 352 containing resistance genes from Zea diploperennis. Six generations, P1, P2, F1, F2, BC1P1 and BC1P2 derived from a cross between resistant line, TZdEI 352 and susceptible line, TZdEI 425 were screened under artificial Striga infestation at Mokwa and Abuja, Nigeria, 2015. Additive‐dominance model was adequate in describing observed variations in the number of emerged Striga plants among the population; hence, digenic epistatic model was adopted for Striga damage. Dominance effects were higher than the additive effects for the number of emerged Striga plants at both locations signifying that non‐additive gene action conditioned inheritance of Striga resistance. Inbred TZdEI 352 could serve as invaluable parent for hybrid development in Striga endemic agro‐ecologies of sub‐Saharan Africa.Item Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program(Springer, 2021) Atanda, S.A.; Olsen, M.; Burgueño, J.; Crossa, J.; Dzidzienyo, D.; Beyene, Y.; Gowda, M.; Dreher, K.; Zhang, X.; Prasanna, B.M.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.R.The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GPItem Prevalence of fungi and aflatoxin contamination in stored groundnut in Ghana(Food Control, 2019-04) Asare Bediako, K.; Dzidzienyo, D.; Ofori, K.; Offei, S.K.; Asibuo, J.Y.; Adu Amoah, R.; Obeng, J.Fungal species associated with stored groundnuts together with incidence of aflatoxin contamination in four major growing regions of Ghana were investigated in this study. Aspergillus niger (39.9%) and A. flavus (26.3%) were the predominant species recovered respectively from 73.3% to 83.3% of 60 groundnut samples used for microbial study. A. flavus was found in equal proportion in the four regions under investigation. Other fungal species identified in the study were Colletotrichum (13.3%), Rhizopus (14.8%), Penicillium (5.4%), Curvularia (0.2%) and A. ochraceus (0.1%). A total of 120 samples were assayed for total aflatoxin using HPLC with fluorescence detection. High contamination of groundnut samples (LOD-928.7, mean = 25.0 ng/g) was detected across the four regions with 20.8% exceeding the threshold limit of 20 ng/g total aflatoxin set by the Ghana Standards Authority. A significant positive correlation (0.59, p < 0.01) was observed between A. flavus and total aflatoxin in groundnut samples, an indication that A. flavus was the major causal agent of aflatoxin contamination of groundnut samples in this study. Further, other potential mycotoxigenic fungi were found in groundnut samples suggesting the need to investigate groundnuts in Ghana for contamination by other mycotoxins.Item Prevalence of fungi and aflatoxin contamination in stored groundnut in Ghana(Food Control, 2019-04) Asare Bediako, K.; Dzidzienyo, D.; Ofori, K.; Offei, S.K.; Asibuo, J.Y.; Adu Amoah, R.; Obeng, J.Fungal species associated with stored groundnuts together with incidence of aflatoxin contamination in four major growing regions of Ghana were investigated in this study. Aspergillus niger (39.9%)and A. flavus (26.3%)were the predominant species recovered respectively from 73.3% to 83.3% of 60 groundnut samples used for microbial study. A. flavus was found in equal proportion in the four regions under investigation. Other fungal species identified in the study were Colletotrichum (13.3%), Rhizopus (14.8%), Penicillium (5.4%), Curvularia (0.2%)and A. ochraceus (0.1%). A total of 120 samples were assayed for total aflatoxin using HPLC with fluorescence detection. High contamination of groundnut samples (LOD-928.7, mean = 25.0 ng/g)was detected across the four regions with 20.8% exceeding the threshold limit of 20 ng/g total aflatoxin set by the Ghana Standards Authority. A significant positive correlation (0.59, p < 0.01)was observed between A. flavus and total aflatoxin in groundnut samples, an indication that A. flavus was the major causal agent of aflatoxin contamination of groundnut samples in this study. Further, other potential mycotoxigenic fungi were found in groundnut samples suggesting the need to investigate groundnuts in Ghana for contamination by other mycotoxins. © 2019 Elsevier LtdItem Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage(springer, 2021) Atanda, S.A.; Olsen, M.; Crossa, J.; Burgueño, J.; Rincent, R.; Dzidzienyo, D.; Beyene, Y.; Gowda, M.; Dreher, K.; Boddupalli, P.M.; Tongoona, P.; Danquah, E.Y.; Olaoye, G.; Robbins, K.R.To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.