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

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 (Ho) and expected (He) 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.

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.

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

BACKGROUND: 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 R2) 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 (R2 = 0.56, P = 0.008) and stretchability (R2 = 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 & Sons Ltd on behalf of Society of Chemical Industry.

Exploring genetic variability, heritability, and trait correlations in gari and eba quality from diverse cassava varieties in Nigeria.

BACKGROUND: Gari (especially in Nigeria) is an important West African food product made from cassava. It is an affordable, precooked, dry, easy to prepare and store food product. Eba is a stiff dough produced by reconstituting gari in hot water. Gari and eba quality is an important driver of varietal acceptance by farmers, processors, and consumers. RESULTS: This study characterized the genetic variability, heritability, and correlations among quality-related traits of fresh roots, gari, and eba. Thirty-three diverse genotypes, including landraces and released and advanced breeding genotypes, were used in this study. In total, 40 traits categorized into fresh root quality, colour, functional, and texture properties trait groups were assessed. We observed broad phenotypic variability among the genotypes used in this study. Dry matter content had a positive (P < 0.05) correlation with gari%, bulk density and a negative correlation with eba hardness and gumminess. Broad-sense heritability across all environments varied considerably among the different trait groups: 62% to 79% for fresh root quality, 0% to 96% for colour, 0% to 79% for functional and 0% to 57% for texture properties. CONCLUSIONS: The stable broad-sense heritability found for gari%, gari and eba colour, bulk density, swelling index, and hardness measured using instrumental texture profile analysis coupled with sufficient variability in the population indicate good potential for genetic improvement of these traits through recurrent selection. Also, it is possible to genetically improve gari%, bulk density, and swelling power by simultaneously improving the dry matter content of fresh roots. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Predicting starch content in cassava fresh roots using near-infrared spectroscopy.

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, (R2 P): 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.

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

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.

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.

Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava.

KEY MESSAGE: More than 40 QTLs associated with 14 stress-related, quality and agro-morphological traits were identified. A catalogue of favourable SNP markers for MAS and a list of candidate genes are provided. Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5130 clones developed at the International Institute of Tropical Agriculture-Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type) were investigated. We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.

Genome-wide diversity and structure variation among lablab [Lablab purpureus (L.) Sweet] accessions and their implication in a Forage breeding program.

Most orphan crops have not been fully sequenced, hence we rely on genome sequences of related species to align markers to different chromosomes. This hinders their utilisation in plant population improvement programs. Utilising the advances in the science of sequencing technologies, the population structure, relatedness, and genetic diversity among accessions can be assessed quickly for better exploitation in forage breeding programs. Using DArTseq technology, we studied the genetic and structural variation in 65 Lablab purpureus (L.) Sweet conserved gene-bank accessions using 9320 DArTseq-based SNPs and 15,719 SilicoDart markers. These markers had a low discriminating ability with mean polymorphic information content (P.I.C.) of 0.14 with DArTseq-based SNPs and 0.13 with SilicoDart markers. However, the markers had a high mean call rate of 73% with DArTseq-based SNPs and 97% with SilicoDart markers. Analysis of molecular variance revealed a high within populations variance (99.4%), indicating a high gene exchange or low genetic differentiation (PhiPT = 0.0057) among the populations. Structure analysis showed three allelic pools in variable clusters of ΔK = 3 and 6. Phylogenetic tree of lablab accessions showed three main groups with variable membership coefficients. Most pairs of accessions (40.3%) had genetic distances between 0.10 and 0.15 for SilicoDart markers, while for DArTseq-based SNPs, (46.5%) had genetic distances between 0.20 and 0.25. Phylogenetic clustering and minimum spanning analysis divided the 65 accessions into three groups, irrespective of their origin. For the first time, this study produced high-density markers with good genom coverage. The utilisation of these accessions in a forage program will base on the information from molecular-based grouping. The outcomes uncovered the presence of noteworthy measure of variety in Uganda, CIAT and ILRI accessions, thus demonstrating an opportunity for further marker-trait-association studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10722-021-01171-y.

Breeding Schemes: What Are They, How to Formalize Them, and How to Improve Them?

Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers.

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa.

Cassava is crucial for food security of millions of people in sub-Saharan Africa. The crop has great potential to contribute to African development and is increasing its income-earning potential for small-scale farmers and related value chains on the continent. Therefore, it is critical to increase cassava production, as well as its quality attributes. Technological innovations offer great potential to drive this envisioned change. This paper highlights genomic tools and resources available in cassava. The paper also provides a glimpse of how these resources have been used to screen and understand the pattern of cassava genetic diversity on the continent. Here, we reviewed the approaches currently used for phenotyping cassava traits, highlighting the methodologies used to link genotypic and phenotypic information, dissect the genetics architecture of key cassava traits, and identify quantitative trait loci/markers significantly associated with those traits. Additionally, we examined how knowledge acquired is utilized to contribute to crop improvement. We explored major approaches applied in the field of molecular breeding for cassava, their promises, and limitations. We also examined the role of national agricultural research systems as key partners for sustainable cassava production.

Enteric methane emissions of grazing short-horn zebu weaner bulls vary with estimation method and level of crude protein supplementation.

Low nutrient intake of short-horn zebu (SHZ) cattle raised under extensive grazing systems results in delayed attainment of mature market weights and consequently increased methane production over time. The purpose of this study was to evaluate and compare the effect of providing grazing SHZ weaner bulls with a supplement varying in crude protein on methane emission using the Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a modified Tier 3 approaches. Weaner bulls were allocated to four treatments: grazing only (GZ), grazing plus a supplement containing either 90 (GZS_90), 110 (GZS_110), or 130 g CP/kg DM (GZS_130). Across treatments, monthly CH4 emissions increased with time. Based on the IPCC Tier 2 method, the highest CH4 production from cattle was estimated from weaner bulls on GZ, while the Tier 3 estimated the highest CH4 production to be from cattle on GZS_130. Although the Tier 2 approach showed no significant difference between treatments, the Tier 3 showed that weaner bulls raised under GZS_130 had the highest production of methane as percentage of gross energy intake. Irrespective of method, weaner bulls on GZ produced more (P < 0.001) CH4 per kilogram live weight gain than supplemented cattle. Compared with Tier 3, the Tier 2 estimated 22.9% higher CH4 per kilogram live weight gain for weaner bulls on GZ. These data suggest that supplementing SHZ weaner bulls with a supplement containing110 g CP/kg DM reduces methane emission per kilogram of live weight gain by 30.7%.

Genome-wide association mapping and genomic prediction for CBSD resistance in Manihot esculenta.

Cassava (Manihot esculenta Crantz) is an important security crop that faces severe yield loses due to cassava brown streak disease (CBSD). Motivated by the slow progress of conventional breeding, genetic improvement of cassava is undergoing rapid change due to the implementation of quantitative trait loci mapping, Genome-wide association mapping (GWAS), and genomic selection (GS). In this study, two breeding panels were genotyped for SNP markers using genotyping by sequencing and phenotyped for foliar and CBSD root symptoms at five locations in Uganda. Our GWAS study found two regions associated to CBSD, one on chromosome 4 which co-localizes with a Manihot glaziovii introgression segment and one on chromosome 11, which contains a cluster of nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. We evaluated the potential of GS to improve CBSD resistance by assessing the accuracy of seven prediction models. Predictive accuracy values varied between CBSD foliar severity traits at 3 months after planting (MAP) (0.27-0.32), 6 MAP (0.40-0.42) and root severity (0.31-0.42). For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. Our results provide an insight into the genetics of CBSD resistance to guide CBSD marker-assisted breeding and highlight the potential of GS to improve cassava breeding.