Musa species, AAB genome group, commonly known as Sukali Ndizi (SND) in Uganda, has attained a substantial commercial value in the recent past owing to its superior fruit quality attributes and better prices. However, its sustainable production and productivity are highly threatened by Fusarium wilt. To facilitate large scale area expansion of this important dessert banana, the present study was carried out to identify the near-ideotypic lines of best quality fruit traits that are also resistant to Fusarium wilt. Nineteen SND ecotypes were subsequently collected from nine key SND growing districts of Uganda and evaluated in the field and laboratory for different fruit quality attributes and response to Fusarium wilt. Results showed a wide diversity among SND ecotypes for fruit-quality traits (fruit pulp texture, flavor and taste). The ecotypes were, however, not significantly different (p > 0.05) for susceptibility to FOC race 1. Cluster analysis based on organoleptic and physio-chemical properties grouped the 19 ecotypes into two major-clusters, each of which was also split into two sub-clusters. Individual subclusters summarize levels of similarity amongst the different ecotypes. The study confirmed the presence of diversity in SND germplasm that could be exploited for SND genetic improvement of the crop through hybridization and selection.
Background: Sukali Ndizi quality traits such as Total soluble solid (TSS) content, pulp texture and sugar/acid (S/A) ratio are critical in quality assessment. Screening very large numbers of fruit genotypes has prompted the development of a high throughput method using Near Infrared spectrometry (NIRS). Results: The calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure. Calibration equations for all analytical characteristics were computed by NIR Software ISI Present WINISI using Modified Partial Least Squares (MPLS) and Partial Least Squares. The quality of calibration models were evaluated by Standard Error of Calibration and coefficient of determination parameters between the measured and the predicted values. The results obtained with FOSS NIR systems 2500 spectrometer (model DS 2500) using the 350-2500 nm range, showed good prediction of the quality traits TSS content, pulp texture and S/A ratio. The MPLS method produced satisfactory Calibration model performance for TSS, texture and S/A ratio, with typical Rc2 of 0.73%Brix, 0.69kgf and 0.7; and root mean squared standard error of calibration of 0.73%Brix, 0.25kgf and 5.36 respectively. This is a good set of quality traits predicting Sukali Ndizi quality with NIRS with robustness, as it was obtained by using diverse Ndizi populations. Conclusions: This can be a useful tool to phenotype large numbers of Ndizi hybrids per day, making it possible to reduce on the resources spent when utilizing organoleptic evaluation selection technique.
Fusarium wilt of bananas (Musa species) is caused by Fusarium oxysporum f. sp. cubense (Foc). Foc race 1 in particular affects dessert bananas in Uganda, causing >60% yield loss. This study was conducted to assess the performance of two new apple banana genotypes for bunch yield, resistance to Foc race 1 and consumer acceptability. The new apple banana genotypes (NAMU1 and NAMU2), along with two check cultivars, one susceptible but preferred by consumers (Sukali ndiizi) and the other resistant (Yangambi-KM5), were evaluated at the National Agricultural Research Laboratories in Uganda. Bunch yields of the two new apple bananas were higher than those of check cultivars by >50%. NAMU1 and Yangambi-KM5 showed no symptoms of Foc race 1, whereas NAMU2 showed mild symptoms on its corms. Sukali ndiizi showed severe pseudostem splitting and corm discoloration as the key symptoms of Foc race 1. The consumer acceptability of NAMU1 and NAMU2 was as high as that of Sukali ndiizi, implying that they can be perfect substitutes for the Foc race 1 susceptible Sukali ndiizi.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.