Browsing by Author "Michael Kanaabi"

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Connecting data for consumer preferences, food quality, and breeding in support of market-oriented breeding of root, tuber, and banana crops
(Journal of The Science of Food and Agriculture, 2023-05-24) Elizabeth Arnaud; Naama Menda; Thierry Tran; Amos Asiimwe; Michael Kanaabi; Karima Meghar; Lora Forsythe; Robert Kawuki; Bryan Ellebrock; Ismail Siraj Kayondo; Afolabi Agbona; Xiaofei Zhang; Thiago Mendes; Marie-Angélique Laporte; Mariam Nakitto; Reuben Tendo Ssali; Asrat Asfaw; Brigitte Uwimana; Chukwudi E. Ogbete; Godwill Makunde; Isabelle Maraval; Lukas A. Mueller; Alexandre Bouniol; Eglantine Fauvelle; Dominique Dufour
The 5-year project ‘Breeding roots, tubers and banana products for end user preferences’ (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centra- lised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials.
Development and validation of near-infraredspectroscopy procedures for prediction ofcassava root dry matter and amylose contentsin Ugandan cassava germplasm
(Journal of The Science of Food and Agriculture, 2023-11-23) Ephraim Nuwamanya; Enoch Wembabazi; Michael Kanaabi; Fatumah Babirye Namakula; Arnold Katungisa; Ivan Lyatumi; Williams Esuma; Emmanuel Oladeji Alamu; Dominique Dufour; Robert Kawuki; Fabrice Davrieux
BACKGROUND: Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter con- tent (DMC) and the starch fraction of amylose content (AC). Accordingly, in the present study, near-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda. RESULTS: Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45 g 100g−1, whereas, for amylose content, it ranged from 14 to 33 g 100g−1. In the validation set, average DMC was 29.5 g 100g−1, whereas, for amylose content, it was 24.64 g 100g−1. For DMC, a modified partial least square regression model had regression coefficients (R2) of 0.98 and 0.96, respectively, in the calibration and validation set. These were also associated with low bias (−0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9 g 100g−1 to 1.06 g 100g−1. For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to −0.03 and a ratio of performance deviation of 4.23 were observed. CONCLUSION: These findings confirm the robustness of NIRS in the estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations.
Flowering and fruit-set in cassava under extended red-light photoperiod supplemented with plant-growth regulators and pruning
(BMC Plant Biology, 2023-06-23) Julius K. Baguma; Settumba B Mukasa; Ephraim Nuwamanya; Titus Alicai; Christopher Omongo; Peter T. Hyde; Tim L. Setter; Mildred Ochwo‐Ssemakula; William Esuma; Michael Kanaabi; Paula Iragaba; Yona Baguma; Robert S. Kawuki
Background Cassava (Manihot esculenta Crantz) is staple food and major source of calories for over 500 million people in sub‐Saharan Africa. The crop is also a source of income for smallholder farmers, and has increasing potential for industrial utilization. However, breeding efforts to match the increasing demand of cassava are impeded by its inability to flower, delayed or unsynchronized flowering, low proportion of female flowers and high fruit abortions. To overcome these sexual reproductive bottlenecks, this study investigated the effectiveness of using red lights to extend the photoperiod (RLE), as a gateway to enhancing flowering and fruit set under field conditions. Materials and methods Panels of cassava genotypes, with non‐ or late and early flowering response, 10 in each case, were subjected to RLE from dusk to dawn. RLE was further evaluated at low (LL), medium (ML) and high (HL) red light intensities, at ~ ≤ 0.5; 1.0 and 1.5PFD (Photon Flux Density) in μmol m−2 s−1 respectively. Additionally, the effect of a cytokinin and anti‐ethylene as plant growth regulators (PGR) and pruning under RLE treatment were examined. Results RLE stimulated earlier flower initiation in all genotypes, by up to 2 months in the late‐flowering genotypes. Height and number of nodes at first branching, particularly in the late‐flowering genotypes were also reduced, by over 50%. Number and proportion of pistillate flowers more than doubled, while number of fruits and seeds also increased. Number of branching levels during the crop season also increased by about three. Earlier flowering in many genotypes was most elicited at LL to ML intensities. Additive effects on flower numbers were detected between RLE, PGR and pruning applications. PGR and pruning treatments further increased number and proportion of pistillate flowers and fruits. Plants subjected to PGR and pruning, developed bisexual flowers and exhibited feminization of staminate flowers. Pruning at first branching resulted in higher pistillate flower induction than at second branching. Conclusions These results indicate that RLE improves flowering in cassava, and its effectiveness is enhanced when PGR and pruning are applied. Thus, deployment of these technologies in breeding programs could significantly enhance cassava hybridizations and thus cassava breeding efficiency and impact.
Genetic dissection of cassava brown streak disease in a genomic selection population
(Frontiers in Plant Science, 2023-01-13) Leah Nandudu; Robert Kawuki; Alex Ogbonna; Michael Kanaabi; Jean-Luc Jannink
Introduction: Cassava brown streak disease (CBSD) is a major threat to food security in East and central Africa. Breeding for resistance against CBSD is the most economical and sustainable way of addressing this challenge. Methods: This study seeks to assess the (1) performance of CBSD incidence and severity; (2) identify genomic regions associated with CBSD traits and (3) candidate genes in the regions of interest, in the Cycle 2 population of the National Crops Resources Research Institute. Results: A total of 302 diverse clones were screened, revealing that CBSD incidence across growing seasons was 44%. Severity scores for both foliar and root symptoms ranged from 1.28 to 1.99 and 1.75 to 2.28, respectively across seasons. Broad sense heritability ranged from low to high (0.15 - 0.96), while narrow sense heritability ranged from low to moderate (0.03 - 0.61). Five QTLs, explaining approximately 19% phenotypic variation were identified for CBSD severity at 3 months after planting on chromosomes 1, 13, and 18 in the univariate GWAS analysis. Multivariate GWAS analysis identified 17 QTLs that were consistent with the univariate analysis including additional QTLs on chromosome 6. Seventy-seven genes were identified in these regions with functions such as catalytic activity, ATP-dependent activity, binding, response to stimulus, translation regulator activity, transporter activity among others. Discussion: These results suggest variation in virulence in the C2 population, largely due to genetics and annotated genes in these QTLs regions may play critical roles in virus initiation and replication, thus increasing susceptibility to CBSD.
NIRS Predictions, Phenotypic Variability and Optimization of Cooking Time for Evaluation of the Root Softness of Boiled Cassava
(National Agricultural Research Organisation, 2023-09-01) Babirye Fatumah Namakula; Ephraim Nuwamanya; Michael Kanaabi; Paul Gibson; Enoch Wembabazi; Iragaba Paula; Robert Sezi Kawuki
This study aimed at quantifying the extent of genetic variability of softness in cassava germplasm across varied cooking times and root sections. It also examined the possibility of using Near Infrared Spectroscopy (NIRS) for measurement of cassava root softness. Softness was evaluated using a penetrometer. This was done at 15, 30 and 45minutes cooking time, all across proximal, middle and distal root sections. These measurements were done on 57 accessions. For each sample, spectra were acquired using NIRS Benchtop (FOSS DS2500) on a composite of each root section of mashed fresh cassava sample. Modified Partial Least Squares regression (MPLS) was used for NIRS calibration development using WINISI software. Significant (P < 0.001) variability in softness was established. Cooking time significantly influenced softness and there were significant accession and root part interaction (P < 0.001). Wide variability and high heritability (H = 0.8) were found for softness at 30 minutes cooking time. Highest association was found with 30- and 45-minutes cooking time (r = 0.58). Strong association was observed between middle root section with distal (r = 0.74) and proximal (r = 0.73). NIRS softness calibration (R2c) were 0.445, 0.413 and 0.521 for 15-, 30-, and 45-minutes cooking time respectively. NIRS prediction (R2p) were 0.322, 0.192, and 0.390 for 15-, 30-, and 45-minutes cooking time respectively. These results suggest that 30 minutes cooking time and middle root section are optimum for softness phenotyping.
Utility of Ugandan genomic selection cassava breeding populations for prediction of cassava viral disease resistance and yield in West African clones
(Frontiers in Plant Science, 2022-11-23) Alfred A. Ozimati; Williams Esuma; Francis Manze; Paula Iragaba; Michael Kanaabi; Chukwuka Ugochukwu Ano; Chiedozie Egesi; Robert S. Kawuki
Cassava (Manihot esculenta Crantz) is a staple crop for ~800 million people in sub-Saharan Africa. Its production and productivity are being heavily affected by the two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD), impacting greatly on edible root yield. CBSD is currently endemic to central, eastern and southern Africa, if not contained could spread to West Africa the largest cassava producer and consumer in the continent. Genomic selection (GS) has been implemented in Ugandan cassava breeding for accelerated development of virus resistant and high yielding clones. This study leveraged available GS training data in Uganda for pre-emptive CBSD breeding in W. Africa alongside CMD and fresh root yield (FRW). First, we tracked genetic gain through the current three cycles of GS in Uganda. The mean genomic estimated breeding values (GEBVs), indicated general progress from initial cycle zero (C0) to cycle one (C1) and cycle two (C2) for CBSD traits and yield except for CMD. Secondly, we used foliar data of both CBSD and CMD, as well as harvest root necrosis and yield data to perform cross-validation predictions. Cross-validation prediction accuracies of five GS models were tested for each of the three GS cycles and West African (WA) germplasm as a test set. In all cases, cross- validation prediction accuracies were low to moderate, ranging from -0.16 to 0.68 for CBSD traits, -0.27 to 0.57 for CMD and -0.22 to 0.41 for fresh root weight (FRW). Overall, the highest prediction accuracies were recorded in C0 for all traits tested across models and the best performing model in cross-validation was G-BLUP. Lastly, we tested the predictive ability of the Ugandan training sets to predict CBSD in W. African clones. In general, the Ugandan training sets had low prediction accuracies for all traits across models in West African germplasm, varying from -0.18 to 0.1. Based on the findings of this study, the cassava breeding program in Uganda has made progress through application of GS for most target traits, but the utility of the training population for pre-emptive breeding in WA is limiting. In this case, efforts should be devoted to sharing Ugandan germplasm that possess resistance with the W. African breeding programs for hybridization to fully enable deployment of genomic selection as a pre-emptive CBSD breeding strategy in W. Africa.