Explore the vast range of titles available.

Genotype-environment interaction (GEI) presents challenges when aiming to select optimal cassava genotypes, often due to biased genetic estimates. Various strategies have been proposed to address the need for simultaneous improvements in multiple traits, while accounting for performance and yield stability. Among these methods are mean performance and stability (MPS) and the multi-trait mean performance and stability index (MTMPS), both utilizing linear mixed models. This study’s objective was to assess genetic variation and GEI effects on fresh root yield (FRY), along with three primary and three secondary traits. A comprehensive evaluation of 22 genotypes was conducted using a randomized complete block design with three replicates across 47 distinct environments (year x location) in Brazil. The broad-sense heritability (H2) averaged 0.37 for primary traits and 0.44 for secondary traits, with plot based heritability (h2mɡ) consistently exceeding 0.90 for all traits. The high extent of GEI variance (s2 ɡxe) demonstrates the GEI effect on the expression of these traits. The dominant analytic factor (FA3) accounted for over 85% of the total variance, and the communality (ɧ) surpassed 87% for all traits. These values collectively suggest a substantial capacity for genetic variance explanation. In Cluster 1, composed of remarkably productive and stable genotypes for primary traits, genotypes BRS Novo Horizonte and BR11-34-69 emerged as prime candidates for FRY enhancement, while BRS Novo Horizonte and BR12-107 002 were indicated for optimizing dry matter content. Moreover, MTMPS, employing a selection intensity of 30%, identified seven genotypes distinguished by heightened stability. This selection encompassed innovative genotypes chosen based on regression variance index (S2 di, R2, andRMSE) considerations for multiple traits. In essence, incorporating methodologies that account for stability and productive performance can significantly bolster the credibility of recommendations for novel cassava cultivars.

The variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in untested environments using factor analytic multiplicative mixed models (FAMM) within multi-environment trials (METs). We evaluated 22 cassava genotypes for fresh root yield (FRY), dry root yield (DRY), shoot yield (ShY), and dry matter content (DMC) across 55 Brazilian environments. FAMM was applied to estimate genetic values and environmental loads, revealing significant genetic variance, especially for FRY (0.16–0.92) and broad-sense heritability ( H ^ ² ) above 0.70 in advanced yield trials. In joint analyses, analytic factor FA 4 explained over 88% of genetic variation for all traits despite high G×E and data imbalance. Positive genetic correlations were found between environments for ShY and DRY (0.99 and 1.0, respectively), while FRY and DMC showed negative correlations (-0.82 and -0.95). Latent regression analysis identified hybrids adaptable to a range of environments, as well as genotypes suited to specific conditions. Moderate correlations between environmental covariables (rainfall, altitude, solar radiation) and FA model loadings suggest these factors contribute to high G×E interactions, notably for FRY. The FAMM model provided a robust approach to G×E analysis in cassava, yielding practical insights for breeding programs.

Despite the economic and social importance, high-yielding cassava cultivars are only released after extensive research, mainly due to the low multiplication rate. This study aimed to assess the impact of using smaller-sized seed cuttings treated with agrochemicals (8MP) compared to the conventional planting size (16 cm) on genetic parameters, agronomic performance, and the ranking of cassava clones based on yield and growth attributes. The evaluation was carried out in clonal evaluation trial (CET), preliminary yield trial (PYT), and uniform yield trials (UYT). Additionally, a new selection scheme for cassava breeding programs was proposed. A total of 169 clones were evaluated, including 154 improved clones at different stages of selection and 15 local varieties used as checks. Field trials were conducted using both sizes of propagative material (8MP and 16 cm) in each phase of the breeding program. The data were analyzed using mixed models, considering the random effects of genotype and genotype-environment interaction (G×E) to determine variances and heritabilities. Bland-Altman concordance and correlation analysis of selection indices were employed to examine the consistency in the ranking of cassava clones using different seed cutting sizes. The distribution of variance components, heritabilities, means, and range of the 8MP and 16 cm trials in different phases of the cassava breeding program exhibited remarkable similarity, thereby enabling a comparative assessment of similar genetic effects. With a selection intensity of 30%, the concordance in clone ranking was 0.41, 0.57, and 0.85 in CET, PYT, and UYT trials, respectively, when comparing the selection based on 8MP and 16 cm trials. It is worth noting that the ranking of the top 15% remained largely unchanged. Based on the findings, proposed changes in the cassava selection scheme involve increasing the number of trials starting from the CET phase, early incorporation of G×E interaction, elimination of the PYT trial, reduction of the breeding cycle from 5 to 3 years, and a decrease in the time required for variety development from 11 to 9 years. These modifications are expected to lead to cost reduction and enhance the effectiveness of cassava breeding programs.

Cassava ( Manihot esculenta Crantz) holds significant importance as one of the world’s key starchy crop species. This study aimed to develop core collections by utilizing both phenotypic data (15 quantitative and 33 qualitative descriptors) and genotypic data (20,023 single-nucleotide polymorphisms) obtained from 1,486 cassava accessions. Six core collections were derived through two optimization strategies based on genetic distances: Average accession-to-nearest-entry and Average entry-to-nearest-entry, along with combinations of phenotypic and genotypic data. The quality of the core collections was evaluated by assessing genetic parameters such as genetic diversity Shannon-Weaver Index, inbreeding ( Fis ), observed ( Ho ), and expected ( Hs ) heterozygosity. While the selection of accessions varied among the six core collections, a seventh collection (consolidated collection) was developed, comprising accessions selected by at least two core collections. Most collections exhibited genetic parameters similar to the complete collection, except for those developed by the Average accession-to-nearest-entry algorithm. However, the variations in the maximum and minimum values of Ho , Hs , and Fis parameters closely resembled the complete collection. The consolidated collection and the collection constructed using genotypic data and the Average entry-to-nearest-entry algorithm (GenEN) retained the highest number of alleles (>97%). Although the differences were not statistically significant (above 5%), the consolidated collection demonstrated a distribution profile and mean trait values most similar to the complete collection, with a few exceptions. The Shannon-Weaver Index of qualitative traits exhibited variations exceeding ±10% when compared to the complete collection. Principal component analysis revealed that the consolidated collection selected cassava accessions with a more uniform dispersion in all four quadrants compared to the other core collections. These findings highlight the development of optimized and valuable core collections for efficient breeding programs and genomic association studies.

Cassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.

Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits (CC_Root_quality). The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes (CC_Root_quality), and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon-Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection (CC_Disease, CC_Root_quality, and CC_Disease, respectively). A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts.

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava?s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm?s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ~14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers? effects that were trained with data from other research institutes/country's germplasm to estimate their clones? GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm?s sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA?s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.