Population genetic structure of the bean leaf beetle Ootheca mutabilis (Coleoptera: Chrysomelidae) in Uganda

Bean leaf beetle (BLB) (Ootheca mutabilis) has emerged as an important bean pest in Uganda, leading to devastating crop losses. There is limited information on the population genetic structure of BLB despite its importance. In this study, novel microsatellite DNA markers and the partial mitochondrial cytochrome oxidase subunit I (mtCOI) gene sequences were used to analyze the spatial population genetic structure, genetic differentiation and haplotype diversity of 86 O. mutabilis samples from 16 (districts) populations. We identified 19,356 simple sequence repeats (SSRs) (mono, di-, tri-, tetra-, penta-, and hexa-nucleotides) of which 81 di, tri and tetra-nucleotides were selected for primer synthesis. Five highly polymorphic SSR markers (4–21 alleles, heterozygosity 0.59–0.84, polymorphic information content (PIC) 50.13–83.14%) were used for this study. Analyses of the 16 O. mutabilis populations with these five novel SSRs found nearly all the genetic variation occurring within populations and there was no evidence of genetic differentiation detected for both types of markers. Also, there was no evidence of isolation by distance between geographical and genetic distances for SSR data and mtCOI data except in one agro-ecological zone for mtCOI data. Bayesian clustering identified a signature of admixture that suggests genetic contributions from two hypothetical ancestral genetic lineages for both types of markers, and the minimum-spanning haplotype network showed low differentiation in minor haplotypes from the most common haplotype with the most common haplotype occurring in all the 16 districts. A lack of genetic differentiation indicates unrestricted migrations between populations. This information will contribute to the design of BLB control strategie

Morphological and genetic characterization of jackfruit (Artocarpus heterophyllus) in the Kayunga and Luwero districts of Uganda

Abstract Background Jackfruit (Artocarpus heterophyllus) is an economically valuable fruit tree in Uganda. However, the production of jackfruit in Uganda is low. Additionally, because of deforestation, genetic erosion of the resource is predicted before its exploitation for crop improvement and conservation. As a prerequisite for crop improvement and conservation, 100 A. heterophyllus tree isolates from the Kayunga and Luwero districts in Uganda were characterized using 16 morphological and 10 microsatellite markers. Results The results from the morphological analysis revealed variations in tree height, diameter at breast height (DBH), and crown diameter, with coefficient of variation (CV) values of 20%, 41%, and 33%, respectively. Apart from the pulp taste, variation was also observed in qualitative traits, including tree vigor, trunk surface, branching density, tree growth habit, crown shape, leaf blade shape, fruit shape, fruit surface, flake shape, flake color, flake flavor and pulp consistency/texture. Genotyping revealed that the number of alleles amplified per microsatellite locus ranged from 2 to 5, with an average of 2.90 and a total of 29. The mean observed (H o ) and expected (H e ) heterozygosity were 0.71 and 0.57, respectively. Analysis of molecular variance (AMOVA) indicated that 81% of the variation occurred within individual trees, 19% among trees within populations and 0% between the two populations. The gene flow (Nm) in the two populations was 88.72. The results from the ‘partitioning around medoids’ (PAM), principal coordinate analysis (PCoA) and genetic cluster analysis further revealed no differentiation of the jackfruit populations. The Mantel test revealed a negligible correlation between the morphological and genetic distances. Conclusions Both morphological and genetic analyses revealed variation in jackfruit within a single interbreeding population. This diversity can be exploited to establish breeding and conservation strategies to increase the production of jackfruit and hence boost farmers’ incomes. However, selecting germplasm based on morphology alone may be misleading

Training Population Optimization for Prediction of Cassava Brown Streak Disease Resistance in West African Clones

Cassava production in the central, southern and eastern parts of Africa is under threat by cassava brown streak virus (CBSV). Yield losses of up to 100% occur in cases of severe infections of edible roots. Easy illegal movement of planting materials across African countries, and long-range movement of the virus vector (Bemisia tabaci) may facilitate spread of CBSV to West Africa. Thus, effort to pre-emptively breed for CBSD resistance in W. Africa is critical. Genomic selection (GS) has become the main approach for cassava breeding, as costs of genotyping per sample have declined. Using phenotypic and genotypic data (genotyping-by-sequencing), followed by imputation to whole genome sequence (WGS) for 922 clones from National Crops Resources Research Institute, Namulonge, Uganda as a training population (TP), we predicted CBSD symptoms for 35 genotyped W. African clones, evaluated in Uganda. The highest prediction accuracy (r = 0.44) was observed for cassava brown streak disease severity scored at three months (CBSD3s) in the W. African clones using WGS-imputed markers. Optimized TPs gave higher prediction accuracies for CBSD3s and CBSD6s than random TPs of the same size. Inclusion of CBSD QTL chromosome markers as kernels, increased prediction accuracies for CBSD3s and CBSD6s. Similarly, WGS imputation of markers increased prediction accuracies for CBSD3s and for cassava brown streak disease root severity (CBSDRs), but not for CBSD6s. Based on these results we recommend TP optimization, inclusion of CBSD QTL markers in genomic prediction models, and the use of high-density (WGS-imputed) markers for CBSD predictions across population

Cut, Root, and Grow: Simplifying Cassava Propagation to Scale

Cassava (Manihot esculenta Crantz) is an essential crop with increasing importance for food supply and as raw material for industrial processing. The crop is vegetatively propagated through stem cuttings taken at the end of the growing cycle and its low multiplication rate and the high cost of stem transportation are detrimental to the increasing demand for high-quality cassava planting materials. Rapid multiplication of vegetative propagules of crops comprises tissue culture (TC) and semi-autotroph hydroponics (SAH) that provide cost-effective propagation of plant materials; however, they contrast the need for specific infrastructure, special media and substrates, and trained personnel. Traditional methods such as TC and SAH have shown promise in efficient plant material propagation. Nonetheless, these techniques necessitate specific infrastructure, specialized media and substrates, as well as trained personnel. Moreover, losses during the intermediate nursery and adaptation stages limit the overall effectiveness of these methods. Building upon an earlier report from Embrapa Brazil, which utilized mature buds from cassava for rapid propagation, we present a modified protocol that simplifies the process for wider adoption. Our method involves excising single nodes with attached leaves from immature (green) cassava stems at 2 months after planting (MAP). These nodes are then germinated in pure water, eliminating the need for specific growth substrates and additional treatments. After the initial phase, the rooted sprouts are transferred into soil within 1–8 weeks. The protocol demonstrates a high turnover rate at minimal costs. Due to its simplicity, cost-effectiveness, and robustness, this method holds significant promise as an efficient means of producing cassava planting materials to meet diverse agricultural needs

Exploring genotype by environment interaction on cassava yield and yield related traits using classical statistical methods

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

Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour

Abstract Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties

Genome‐wide association study for yield and quality of granulated cassava processed product

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

Data_Sheet_1_Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta).PDF

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.</p

Data_Sheet_2_Parsimonious genotype by environment interaction covariance models for cassava (Manihot esculenta).xlsx

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.</p

Cassava retting ability and textural attributes of fufu for demand‐driven cassava breeding

International audienceBACKGROUND Cassava retting ability and the textural qualities of cooked fufu are important quality traits. Cassava retting is a complex process in which soaking causes tissue breakdown, starch release, and softening. The rate at which various traits linked to it evolve varies greatly during fufu processing. According to the literature, there is no standard approach for determining retting ability. The retting indices and textural properties of fufu were measured using both manual and instrumental approaches. RESULTS Different protocols were developed to classify 64 and 11 cassava genotypes into various groups based on retting ability and textural qualities, respectively. The retting protocols revealed considerable genetic dissimilarities in genotype classification: foaming ability and water clarity should be measured at 24 h, while penetrometer, hardness, turbidity, pH, and total titratable acidity data are best collected after 36 h. The stepwise regression model revealed that pH, foaming ability, and dry matter content are the best multivariates (with the highest R 2 ) for predicting cassava retting. These predictors were used to develop an index for assessing the retting ability of cassava genotypes. The retting index developed showed a significant relationship with dry matter content and fufu yield. The study also showed significant correlations between instrumental cohesiveness and sensory smoothness ( r = −0.75), moldability ( r = −0.62), and stretchability ( r = 0.78). Instrumental cohesiveness can correctly estimate fufu smoothness ( R 2 = 0.56, P = 0.008) and stretchability ( R 2 = 0.60, P = 0.005). CONCLUSION pH, foaming ability, and dry matter content are the best traits for predicting cassava retting ability, while instrumental cohesiveness can effectively estimate fufu smoothness and stretchability. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry