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Abstract Nitrogen (N) is an important nutrient element needed by cassava for optimum yield and it is a vital component of nucleotides (nucleic acids), enzymes, amino acids (proteins), chlorophyll molecules and hormones, among other essential compounds required for growth and development of cassava. Nitrogen stress is a major cassava production constraint, the study aimed to examine genotype by environment interaction (GEI) effects and fresh root yield stability of 203 diverse cassava clones to identify genotypes with stable performance under low and optimum nitrogen regimes across environments using AMMI and GGE biplot analysis. Experiments were conducted using an augmented block design with three replications for two years in three locations in Nigeria. There were significant differences (p

Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high (RCal2 ranging from 0.72 to 0.99). Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. Overall, the models exhibited a robust fit for all cooking times, showcasing the significant potential of NIRs as a high-throughput phenotyping tool for classifying cassava genotypes based on cooking time.

Abstract The starchy storage roots of cassava are commonly processed into a variety of products, including cassava granulated processed products (gari). The commercial value of cassava roots depends on the yield and quality of processed products, directly influencing the acceptance of new varieties by farmers, processors, and consumers. This study aims to estimate genetic advance through phenotypic selection and identify genomic regions associated and candidate genes linked with gari yield and quality. Higher single nucleotide polymorphism (SNP)‐based heritability estimates compared to broad‐sense heritability estimates were observed for most traits highlighting the influence of genetic factors on observed variation. Using genome‐wide association analysis of 188 clones, genotyped using 53,150 genome‐wide SNPs, nine SNPs located on seven chromosomes were significantly associated with peel loss, gari yield, color parameters for gari and eba, bulk density, swelling index, and textural properties of eba. Future research will focus on validating and understanding the functions of identified genes and their influence on gari yield and quality traits.

The response to the diverse needs along the cassava value chain, the urge to increase genetic gain, and the need for rapid varietal turnover will necessitate not only technological innovations but also transformation of public breeding programs in sub-Saharan Africa (SSA). We developed guiding, flexible and adaptative tools for portfolio management of cassava breeding. The cassava breeding and product development pipeline process was mapped to illustrate activities of each stage, as well as to clarify key decision points. Stakeholders involved at all stages of breeding were identified. This allowed for identification of gaps and new crucial functions. To clarify accountability and reduce complexity in the decision-making at key decision points, the roles were mapped against decision-rights at each stage-gate. Cassava crop calendars for the different regions in SSA were developed to facilitate better planning. A product advancement template was developed to guide product advancement. The tools that have been developed and stage-gate mapping, will support regional efforts to establish more structured, transparent, participatory, efficient, inclusive, and demand-driven cassava breeding in the region. These approaches could be customized to other commodities.

Cassava productivity is constrained by low soil nitrogen, which is predominant in most cassava-growing regions in the tropics and subtropical agroecology. Improving the low nitrogen tolerance of cassava has become an important breeding objective. The current study aimed to develop cassava varieties with improved nitrogen use efficiency by identifying genomic regions and candidate genes linked to nitrogen use efficiency in cassava. A genome-wide association study (GWAS) was performed using the Genome Association and Prediction Integrated Tool (GAPIT). A panel of 265 diverse cassava genotypes was phenotyped for 10 physiological and agronomic traits under optimum and low-nitrogen regimes. Whole-genome genotyping of these cassava cloneswas performed using the Diversity Arrays Technology (DArTseq) sequencing platform. A total of 68,814 single nucleotide polymorphisms (SNPs) were identified, which were spread across the entire 18 chromosomes of the cassava genome, of which 52 SNPs at various densities were found to be associated with nitrogen use efficiency in cassava and other yield-related traits. The putative genes identified through GWAS, especially those with significant associated SNP markers for NUE and related traits have the potential, if deployed appropriately, to develop cassava varieties with improved nitrogen use efficiency, which would translate to a reduction in the economic and environmental cost of cassava production.

A validated marker system is crucial to running an effective genomics-assisted breeding program. We used 36 Kompetitive Allele-Specific PCR (KASP) markers to genotype 376 clones from the biofortified cassava pipeline, and fingerprinted 93 of these clones with DArTseq markers to characterize breeding materials and evaluate their relationships. The discriminating ability of the 36-quality control (QC) KASP and 6602 DArTseq markers was assessed using 92 clones genotyped in both assays. In addition, trait-specific markers were used to determine the presence or absence of target genomic regions. Hierarchical clustering identified two major groups, and the clusters were consistent with the breeding program origins. There was moderate genetic differentiation and a low degree of variation between the identified groups. The general structure of the population was similar using both assays. Nevertheless, KASP markers had poor resolution when it came to differentiating the genotypes by seed sources and overestimated the prevalence of duplicates. The trait-linked markers did not achieve optimal performance as all markers displayed variable levels of false positive and/or false negative. These findings represent the initial step in the application of genomics-assisted breeding for the biofortified cassava pipeline, and will guide the use of genomic selection in the future.

Abstract 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) effects 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 significant influence of genotype, environment, and GEI on the performance of genotypes. Mealiness scores showed no significant relationship with firmness 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 effective 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 significant genetic gains can be achieved in a future hybridization program. The genotype main effects plus genotype × environment interaction (GGE) biplot analysis showed that the different 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, five 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 mealiness.

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.

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.

Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness common among pre-school children and pregnant women in developing countries particularly Africa. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. Single nucleotide polymorphism (SNP) markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for routine marker-assisted selection (MAS). This is due to the lack of useful information on markers’ performances in diverse genetic backgrounds. To overcome this bottleneck, technical and biological validation of the loci associated with increased carotenoid content and dry matter content were carried out using populations independent of the marker discovery population. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific polymerase chain reaction (PCR) assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r< 0.40) due to the more quantitative nature of the trait. This work confirmed the markers’ effectiveness in multiple backgrounds, therefore, further strengthening their value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker validation, thus leading to the more routine use of markers in MAS in cassava improvement programs.

The assessment of cassava clones across multiple environments is often carried out at the uniform yield trial, a late evaluation stage, before variety release. This is to assess the differential response of the varieties across the testing environments, a phenomenon referred to as genotype-by-environment interaction (GEI). This phenomenon is considered a critical challenge confronted by plant breeders in developing crop varieties. This study used the data from variety trials established as randomized complete block design (RCBD) in three replicates across 11 locations in different agro-ecological zones in Nigeria over four cropping seasons (2016–2017, 2017–2018, 2018–2019, and 2019–2020). We evaluated a total of 96 varieties, including five checks, across 48 trials. We exploited the intricate pattern of GEI by fitting variance–covariance structure models on fresh root yield. The goodness-of-fit statistics revealed that the factor analytic model of order 3 (FA3) is the most parsimonious model based on Akaike Information Criterion (AIC). The three-factor loadings from the FA3 model explained, on average across the 27 environments, 53.5% [FA (1)], 14.0% [FA (2)], and 11.5% [FA (3)] of the genetic effect, and altogether accounted for 79.0% of total genetic variability. The association of factor loadings with weather covariates using partial least squares regression (PLSR) revealed that minimum temperature, precipitation and relative humidity are weather conditions influencing the genotypic response across the testing environments in the southern region and maximum temperature, wind speed, and temperature range for those in the northern region of Nigeria. We conclude that the FA3 model identified the common latent factors to dissect and account for complex interaction in multi-environment field trials, and the PLSR is an effective approach for describing GEI variability in the context of multi-environment trials where external environmental covariables are included in modeling.

AbstractModern 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 4/18/2022https://cassavabase.org/https://www.nextgencassava.org/https://github.com/solgenomics/https://hub.docker.com/u/breedbase

Variety advancement decisions for root quality and yield-related traits in cassava are complex due to the variable patterns of genotype-by-environment interactions (GEI). Therefore, studies focused on the dissection of the existing patterns of GEI using linear-bilinear models such as Finlay-Wilkinson (FW), additive main effect and multiplicative interaction (AMMI), and genotype and genotype-by-environment (GGE) interaction models are critical in defining the target population of environments (TPEs) for future testing, selection, and advancement. This study assessed 36 elite cassava clones in 11 locations over three cropping seasons in the cassava breeding program of IITA based in Nigeria to quantify the GEI effects for root quality and yield-related traits. Genetic correlation coefficients and heritability estimates among environments found mostly intermediate to high values indicating high correlations with the major TPE. There was a differential clonal ranking among the environments indicating the existence of GEI as also revealed by the likelihood ratio test (LRT), which further confirmed the statistical model with the heterogeneity of error variances across the environments fit better. For all fitted models, we found the main effects of environment, genotype, and interaction significant for all observed traits except for dry matter content whose GEI sensitivity was marginally significant as found using the FW model. We identified TMS14F1297P0019 and TMEB419 as two topmost stable clones with a sensitivity values of 0.63 and 0.66 respectively using the FW model. However, GGE and AMMI stability value in conjunction with genotype selection index revealed that IITA-TMS-IBA000070 and TMS14F1036P0007 were the top-ranking clones combining both stability and yield performance measures. The AMMI-2 model clustered the testing environments into 6 mega-environments based on winning genotypes for fresh root yield. Alternatively, we identified 3 clusters of testing environments based on genotypic BLUPs derived from the random GEI component.

Cassava breeders have made significant progress in developing new genotypes with improved agronomic characteristics such as improved root yield and resistance against biotic and abiotic stresses. However, these new and improved cassava (Manihot esculenta Crantz) varieties in cultivation in Nigeria have undergone little or no improvement in their culinary qualities; hence, there is a paucity of genetic information regarding the texture of boiled cassava, particularly with respect to its mealiness, the principal sensory quality attribute of boiled cassava roots. The current study aimed at identifying genomic regions and polymorphisms associated with natural variation for root mealiness and other texture-related attributes of boiled cassava roots, which includes fibre, adhesiveness (ADH), taste, aroma, colour, and firmness. We performed a genome-wide association (GWAS) analysis using phenotypic data from a panel of 142 accessions obtained from the National Root Crops Research Institute (NRCRI), Umudike, Nigeria, and a set of 59,792 high-quality single nucleotide polymorphisms (SNPs) distributed across the cassava genome. Through genome-wide association mapping, we identified 80 SNPs that were significantly associated with root mealiness, fibre, adhesiveness, taste, aroma, colour and firmness on chromosomes 1, 4, 5, 6, 10, 13, 17 and 18. We also identified relevant candidate genes that are co-located with peak SNPs linked to these traits in M. esculenta. A survey of the cassava reference genome v6.1 positioned the SNPs on chromosome 13 in the vicinity of Manes.13G026900, a gene recognized as being responsible for cell adhesion and for the mealiness or crispness of vegetables and fruits, and also known to play an important role in cooked potato texture. This study provides the first insights into understanding the underlying genetic basis of boiled cassava root texture. After validation, the markers and candidate genes identified in this novel work could provide important genomic resources for use in marker-assisted selection (MAS) and genomic selection (GS) to accelerate genetic improvement of root mealiness and other culinary qualities in cassava breeding programmes in West Africa, especially in Nigeria, where the consumption of boiled and pounded cassava is low.

Demand-led breeding strategies are gaining importance in public sector breeding globally. While borrowing approaches from the private sector, public sector programs remain mainly focused on food security and social impact related outcomes. This necessitates information on specific user groups and their preferences to build targeted customer and product profiles for informed breeding decisions. A variety of studies have identified gendered trait preferences, but do not systematically analyze differences related to or interactions of gender with other social dimensions, household characteristics, and geographic factors. This study integrates 1000minds survey trait trade-off analysis with the Rural Household Multi-Indicator Survey to study cassava trait preferences in Nigeria related to a major food product, gari. Results build on earlier research demonstrating that women prioritize food product quality traits while men prioritize agronomic traits. We show that food product quality traits are more important for members from food insecure households and gender differences between men and women increase among the food insecure. Furthermore, respondents from poorer households prioritize traits similar to respondents in non-poor households but there are notable trait differences between men and women in poor households. Women in female headed household prioritized quality traits more than women living with a spouse. Important regional differences in trait preferences were also observed. In the South East region, where household use of cassava is important, and connection to larger markets is less developed, quality traits and in ground storability were prioritized more than in other states. These results reinforce the importance of recognizing social difference and the heterogeneity among men and women, and how individual and household characteristics interact to reveal trait preference variability. This information can inform trait prioritization and guide development of breeding products that have higher social impact, which may ultimately serve the more vulnerable and align with development goals.

Cassava mosaic geminiviruses (CMGs) and cassava brown streak viruses (CBSVs) cause the highest yield losses in cassava production in Africa. In particular, cassava brown streak disease (CBSD) is and continues to be a significant constraint to optimal cassava production in Eastern and Southern Africa. While CBSD has not been reported in West Africa, its recent rapid spread and damage to cassava productivity in Eastern, and Southern Africa is alarming. The aim of this study was to evaluate Nigerian cassava genotypes in order to determine their responses to CBSD, in the event that it invades Nigeria, the world’s largest cassava producer. The study gathered information on whether useful CBSD resistance alleles are present in the elite Nigerian cassava accessions. A total of 1,980 full-sib cassava seedlings from 106 families were assessed in the field at the seedling stage for a year. A subset of 569 clones were selected and assessed for another year at the clonal stage in Namulonge, central Uganda, a known hotspot for CBSD screening. Results indicated that foliar and root incidences and severities varied significantly (p ≤ 0.01, p ≤ 0.001) except for CBSD foliar incidence at 6 months (CBSD6i). Highest and lowest plot-based heritability estimates for CBSD were registered for CBSD root severity (CBSDrs) (0.71) and CBSD6i (0.5). Positive and highly significant correlations were noted between CBSD root incidence (CBSDri) and CBSDrs (r = 0.90***). Significant positive correlations were also noted between CBSD foliar severity at 3 months (CBSD3s) and CBSD foliar incidence at 6 months (CBSD6i) (r = 0.77***), CBSD3s and CBSDrs (r = 0.35***). Fresh root weight (FreshRW) negatively correlated with CBSDri and CBSDrs, respectively (r = −0.21*** and r = −0.22***). Similarly, CBSD3s correlated negatively with cassava mosaic disease severity at 3 (CMD3s) and 6 months (CMD6s), respectively (r = −0.25*** and r = −0.21***). Fifteen clones were selected using a non-weighted summation selection index for further screening. In conclusion, results revealed that the elite Nigerian accessions exhibited significant susceptibility to CBSD within 2 years of evaluation period. It is expected that this information will aid future breeding decisions for the improvement of CBSD resistance among the Nigerian cassava varieties.

Background Cassava is an outcrossing, highly heterozygous plant that is reported to suffer from inbreeding depression. However, unraveling recessive traits and exploring additive genes would require a limited level of inbreeding for the genetic improvement of cassava. Method The impact of inbreeding depression (ID) on agronomic and biotic tolerance of cassava was evaluated using the S 1 progenies of five African cassava varieties (TMS 30572, TME 419, TMS 98/0505, TMS 01/1371, and TMS 98/0002) at the seedling and clonal evaluation stages. Results At both trial stages, the effects of ID were severe on average performance of fresh root yield and fresh foliage yield; moderate on harvest index, dry matter content, vigor, and tolerance to cassava mosaic disease, cassava bacterial blight, and cassava anthracnose diseases; and less severe on plant height. Further examination of S 1 families using molecular markers showed varying levels of heterozygosity at several loci across the genome. In addition, the degree of heterozygosity and ID varied by the S 1 family. The TMS 01/1371 family showed the lowest degree of heterozygosity and ID on the average performance of different agronomic attributes, indicating that inbreeding may be strategically explored in this family to increase genetic gain and identify useful recessive traits. Lastly, the observed depression from inbreeding was higher in seedling evaluation than in clonal evaluation trials for fresh root yield, fresh foliage yield, harvest index, and dry matter content. Conclusion S 1 individuals showing relatively low heterozygosity based on SNP data were selected as parents to advance genetic gain in cassava. Generally, reduced heterozygosity was prominent in traits with severe ID impacts on average phenotypic performance. Our results highlight the relative importance of exploring non-additive genetic effects and transgressive segregations for favorable allele combinations in cassava improvement.