Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS)

Portable Vis/NIRS are flexible tools for fast and unbiased analyses of constituents with minimal sample preparation. This study developed calibration models for dry matter content (DMC) and carotenoids in fresh cassava roots using a portable Vis/NIRS system. We examined the effects of eight data pre-treatment combinations on calibration models and assessed calibrations on processed and intact root samples. We compared Vis/NIRS derived-DMC to other phenotyping methods. The results of the study showed that the combination of standard normal variate and de-trend (SNVD) with first derivative calculated on two data points and no smoothing (SNVD+1111) was adequate for a robust model. Calibration performance was higher with processed than the intact root samples for all the traits although intact root models for some traits especially total carotenoid content (TCC) (R2c = 96%, R2cv = 90%, RPD = 3.6 and SECV = 0.63) were sufficient for screening purposes. Using three key quality traits as templates, we developed models with processed fresh root samples. Robust calibrations were established for DMC (R2c = 99%, R2cv = 95%, RPD = 4.5 and SECV = 0.9), TCC (R2c = 99%, R2cv = 91%, RPD = 3.5 and SECV = 2.1) and all Trans β-carotene (ATBC) (R2c = 98%, R2cv = 91%, RPD = 3.5 and SECV = 1.6). Coefficient of determination on independent validation set (R2p) for these traits were also satisfactory for ATBC (91%), TCC (88%) and DMC (80%). Compared to other methods, Vis/NIRS-derived DMC from both intact and processed roots had very high correlation (>0.95) with the ideal oven-drying than from specific gravity method (0.49). There was equally a high correlation (0.94) between the intact and processed Vis/NIRS DMC. Therefore, the portable Vis/NIRS could be employed for the rapid analyses of DMC and quantification of carotenoids in cassava for nutritional and breeding purposes

Growth and yield response of selected improved soybean (Glycine max [L.] Merrill) varieties to varying weeding regimes under a tropical condition

The field trial was conducted at the Teaching and Research Farm, Abia State University, Umuahia Campus, Umudike to study the performance of three highly improved soybean varieties (‘TGX 1835-10E’, ‘TGX 1987-62F’ and ‘TGX 1448-2E’) to different weeding regimes (weed free, inweeded, weeded once, weeded twice and weeded three times) and to estimate character association and contribution toward seed yield per hectare. The experiment was a factorial combination of variety and weeding regimes in randomized complete block design with three replications. Vegetative data which included plant height, number of branches and number of leaves were taken at 10 weeks after planting (WAP) while at harvest, the following yield data: pod length, pod width, number of pods per plant, number of seeds per plant, pod weight per plant, 100 seed weight and seed yield per hectare were taken. The only phenological trait taken was number of days to 50 % flowering. The competing weeds were also identified, sampled, counted, dried, weighed and recorded at 9 WAP and at harvest. Data were analyzed using the procedure outlined for ANOVA and means separated by LSD (P=0.05). Correlation and Path coefficients analyses were also carried out. The results showed a highly significant difference (P<0.01) among the varieties in all the traits studied. ‘TGX 1835-10E’ variety gave the highest seed yield/ha while weed regimes like weed free, weeded twice and three times showed non significantly the best performance in all aspect. The results also showed that plots left inweeded and weeded once inevitably had the highest yield reduction in all the varieties. Plant height, number of branches, number of leaves at 10 WAP, number of seeds and pod weight per plant, 100 seed weight as well as soybean dry weight at 9 WAP showed high positive magnitude and significant (P<0.01) correlations with seed yield per hectare. The highest positive direct effect on yield was recorded in plant height at 10 WAP

Contributions of the VitisGen2 project to grapevine breeding and genetics

The VitisGen projects (2011-2022) have improved the tools available for breeding new grapevine cultivars with regional adaptation, high quality, and disease resistance. VitisGen2 (the second project in the series) was a multi-state collaboration (USDA-Geneva, New York; University of California, Davis; USDA-Parlier, California; Cornell University; Missouri State University; University of Minnesota; South Dakota State University; Washington State University; North Dakota State University; and E&amp;J Gallo, California) to develop improved genetic mapping technology; to identify useful DNA marker-trait associations; and to incorporate marker-assisted selection (MAS) into breeding programs. A novel genetic mapping platform (rhAmpSeq) now provides 2000 + markers that are transferable across the Vitis genus. rhAmpSeq has been used in California, New York, Missouri, and South Dakota to identify new QTL for powdery and downy mildew resistance. In addition, fruit/flower traits that would normally take years to phenotype have been associated with predictive markers accessible from seedling DNA (e.g. malate metabolism, anthocyanin acylation, bloom phenology and flower sex). Since 2011, the project has used MAS to screen thousands of grape seedlings from public breeding programs in the United States and has produced “Ren- Stack” public domain lines to enable simultaneous access to 4 or 6 powdery mildew resistance loci from single source genotypes. High-throughput phenotyping for powdery and downy mildew resistance has been revolutionized with the Blackbird automated-imaging system powered by artificial intelligence for image analysis. Affordable DNA sequencing along with phenotyping innovations are transforming grapevine breeding

Comparison of Near-infrared Spectroscopy with other options for total carotenoids content phenotyping in fresh cassava roots

This study compared the relationship of different phenotyping methods including iCheckTM CAROTENE (iCheck), Chromameter, colour chart and visible/near-infrared spectroscopy (Vis/NIRS) used in quantifying total carotenoids content (TCC) in fresh cassava roots. Using a total of 194 cassava clones harvested from the International Institute of Tropical Agriculture (IITA), Ibadan, we compared the repeatability precision, accuracy of measurement and correlations of these phenotyping methods. From the results, Vis/NIRS-analyzed TCC had high and positive correlations with Chromameter and Color chart (r = 0.91 and 0.71, respectively). On the other hand, the result revealed somewhat moderate correlation (r = 0.67) between Vis/NIRS and iCheck measurements. Vis/NIRS, iCheck and chromameter methods gave high and nearly equal heritability estimates (0.95, 0.98 and 0.98, respectively) illustrating high repeatability precision of these methods; an indication that they can be used for germplasm selection in the early stages of breeding. Conversely, with Bland-Altman plot at 95% confidence level, the accuracy of iCheck was not comparable with that of Vis/ NIRS. The information derived from this analysis directly contributes towards the genetic improvement of root quality traits in cassava and facilitates the sharing of data across cassava breeding consortium

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.Bill & Melinda Gates FoundationUKaidCGIAR Research Program on Roots, Tubers and BananasPeer Revie

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden

HIGH-THROUGHPUT PHENOTYPING AND GENOMICS-ASSISTED BREEDING FOR QUALITY TRAITS IN CASSAVA

To promote rapid and standardized phenotyping for genomic improvement of quality traits in cassava, calibrations for dry matter content (DMC) and carotenoids in fresh cassava roots were developed from a portable near infra-red spectrometer (NIRS). Effect of eight pre-treatment combinations was evaluated on calibration performance and standard normal variate and de-trend (SNVD), with the first derivative calculated on two data points and no smoothing (SNVD+1111), was adequate to build a robust model. Generally, high calibration performance was obtained for most traits e.g. model for DMC on mashed samples had - R2c = 99%, R2cv = 95%, RPD = 4.5 and SECV = 0.9, with satisfactory R2 of 80% on independent validation set. On average, models developed with mashed were better than the intact samples. Intact and mashed NIRS-derived DMC were highly correlated (0.94) and had higher correlations (>0.95) with the ideal oven-drying than the specific gravity methods (0.49 and 0.69, depending on the dataset). Non-linear calibration model using random forest (RF), was equally develop and used to process spectra from National Root Crops Research Institute (NRCRI), Umudike for carotenoids including total carotenoid content (TCC) and some individual carotenoids (ICS): all-trans β-carotene (ATBC), violaxanthin (VIO), Lutein (LUT), 15-Cis beta-carotene (15CBC), 13-Cis beta-carotene (13CBC), Alpha-carotene (AC), 9-Cis beta-carotene (9CBC) and phytoene (PHY) . Derived carotenoids were used to understand correlations (phenotypic and genotypic), especially between TCC and ICS. High and positive phenotypic and genotypic correlations (>0.75) were obtained between TCC and the ICS except for PHY and LUT. Genome-wide association studies identified previously reported region on chromosome 1 associated with variation in TCC, in addition to other unidentified associations for both TCC and the ICS. Evaluating the potential of using Genome-wide predictions for carotenoids improvement, higher predictions were obtained from non-linear RF model with a one-step approach in single and multi-trait scenarios than linear and two-step approaches. The possibility of using molecular markers to assign parentage to progenies from a polycross nursery scheme was demonstrated with 100% assignment accuracy from simulated datasets. The information provided in this study is vital in redefining cassava breeding

Genotype-by-environment interaction and stability of root mealiness and other organoleptic properties of boiled cassava roots

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

Paternity assignment in white guinea yam (Dioscorea Rotundata) half-sib Progenies from polycross mating design using SNP markers

Open Access Journal; Published online: 19 April 2020White Guinea yam is mostly a dioecious outcrossing crop with male and female flowers produced on distinct plants. Fertile parents produce high fruit set in an open pollination polycross block, which is a cost-effective and convenient way of generating variability in yam breeding. However, the pollen parent of progeny from polycross mating is usually unknown. This study aimed to determine paternity in white Guinea yam half-sib progenies from polycross mating design. A total of 394 half-sib progenies from random open pollination involving nine female and three male parents was genotyped with 6602 SNP markers from DArTSeq platform to recover full pedigree. A higher proportion of expected heterozygosity, allelic richness, and evenness were observed in the half-sib progenies. A complete pedigree was established for all progenies from two families (TDr1685 and TDr1688) with 100% accuracy, while in the remaining families, paternity was assigned successfully only for 56 to 98% of the progenies. Our results indicated unequal paternal contribution under natural open pollination in yam, suggesting unequal pollen migrations or gene flow among the crossing parents. A total of 3.8% of progenies lacking paternal identity due to foreign pollen contamination outside the polycross block was observed. This study established the efficient determination of parental reconstruction and allelic contributions in the white Guinea yam half-sib progenies generated from open pollination polycross using SNP markers. Findings are useful for parental reconstruction, accurate dissection of the genetic effects, and selection in white Guinea yam breeding program utilizing polycross mating design