Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.

East African highland bananas (EAHB) were regarded as sterile. Their screening for female fertility with “Calcutta 4” as male parent revealed that 37 EAHB were fertile. This was the foundation for the establishment of the EAHB crossbreeding programs by the International Institute of Tropical Agriculture (IITA) and the National Agricultural Research Organization (NARO) in Uganda in the mid-1990s. The aim of this study was to assess the progress and efficiency of the EAHB breeding program at IITA, Sendusu in Uganda. Data on pollinations, seeds generated and germinated, plus hybrids selected between 1995 and 2015 were analyzed. Pollination success and seed germination percentages for different cross combinations were calculated. The month of pollination did not result in significantly different (P = 0.501) pollination success. Musa acuminata subsp. malaccensis accession 250 had the highest pollination success (66.8%), followed by the cultivar “Rose” (66.6%) among the diploid males. Twenty-five EAHB out of 41 studied for female fertility produced up to 305 seeds per pollinated bunch, and were therefore deemed fertile. The percentage of seed germination varied among crosses: 26% for 2x × 4x, 23% for 2x × 2x, 11% for 3x × 2x, and 7% for 4x × 2x. Twenty-seven NARITA hybrids (mostly secondary triploids ensuing from the 4x × 2x) were selected for further evaluation in the East African region. One so far –“NARITA 7”– was officially released to farmers in Uganda. Although pollination of EAHB can be conducted throughout the year, the seed set and germination is low. Thus, further research on pollination conditions and optimization of embryo culture protocols should be done to boost seed set and embryo germination, respectively. More research in floral biology and seed germination as well as other breeding strategies are required to increase the efficiency of the EAHB breeding program.

Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.

Vascular wilt caused by the ascomycete fungal pathogen Fusarium oxysporum f. sp. cubense (Foc) is a major constraint of banana production around the world. The virulent race, namely Tropical Race 4, can infect all Cavendish-type banana plants and is now widespread across the globe, causing devastating losses to global banana production. In this study, we characterized Foc Subtropical Race 4 (STR4) resistance in a wild banana relative which, through estimated genome size and ancestry analysis, was confirmed to be Musa acuminata ssp. malaccensis. Using a self-derived F₂ population segregating for STR4 resistance, quantitative trait loci sequencing (QTL-seq) was performed on bulks consisting of resistant and susceptible individuals. Changes in SNP index between the bulks revealed a major QTL located on the distal end of the long arm of chromosome 3. Multiple resistance genes are present in this region. Identification of chromosome regions conferring resistance to Foc can facilitate marker assisted selection in breeding programs and paves the way towards identifying genes underpinning resistance.

Fusarium wilt of bananas (Musaspp.), caused byFusarium oxysporumf. sp.cubense(Foc) causes up to 100% yield loss in bananas.Focrace 1 in particular is very devastating to dessert bananas in Uganda. One of the effective control strategies for the disease is the development of resistant cultivars through breeding. The objectives of this study were to identify suitable banana germplasm for generating a segregating population for resistance toFocrace 1 and understand the mode of inheritance of resistance toFocrace 1. Twenty-two banana accessions sourced from the National Agricultural Research Organisation in Uganda were challenged withFocrace 1 in a screen house experiment. Monyet, resistant toFocrace 1 and Kokopo, susceptible, were selected and crossed to generate 142 F₁genotypes. These F₁genotypes were also challenged withFocrace 1 in a screen house experiment. Data were collected on rhizome discoloration index (RDI), leaf symptom index (LSI) and pseudo-stem splitting (PSS), and analysed for variability. The banana accessions evaluated showed varying degrees of resistance toFocrace 1. Segregation ratios for resistant versus susceptible progenies fitted 13:3 (χ² = 0.12,P = 0.73) for RDI and 11:5 (χ²= 3.04,P = 0.08) for PSS. Estimated broad sense heritability was 27.8% for RDI, 13.9% for LSI and 14.7% for PSS. The results suggest that resistance toFocrace 1 in banana is controlled by at least two dominant genes with epistatic interaction and that heritability of resistance toFocrace 1 is low inMusaspp.

Banana (Musa spp.), a perennial (sub-)tropical crop, suffers from seasonal droughts, which are typical of rain-fed agriculture. This study aimed at understanding the effect of seasonal drought on early growth, flowering and yield traits in bananas grown in the East African highlands. A field experiment was set up in North Tanzania using four genotypes from different geographical origins and two different ploidy levels. The treatments considered were exclusively rain-fed versus rain supplemented with irrigation. Growth in plant girth and leaf area were promising traits to detect the early effect of water deficit. Seasonal drought slowed down vegetative growth, thus significantly decreasing plant girth, plant height and the number of suckers produced when compared to irrigated plants. It also delayed flowering time and bunch maturity and had a negative effect on yield traits. However, the results depended on the genotype and crop cycle and their interaction with the treatments. “Nakitengwa”, an East African highland banana (EAHB; AAA genome group), which is adapted to the region, showed sensitivity to drought in terms of reduced bunch weight and expected yield, while “Cachaco” (ABB genome group) showed less sensitivity to drought but had a poorer yield than “Nakitengwa”. Our study confirms that seasonal drought has a negative impact on banana production in East Africa, where EAHBs are the most predominant type of bananas grown in the region. We also show that a drought-tolerant cultivar not adapted to the East African highlands had a low performance in terms of yield. We recommend a large-scale screening of diploid bananas to identify drought-tolerant genotypes to be used in the improvement of locally adapted and accepted varieties.