Search

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.

Conversion of cassava (Manihot esculenta) roots to processed products such as gari and fufu before consumption is a common practice worldwide by cassava end-user for detoxification, prolonged shelf life or profitability. Fresh root and processed product yield are supposed to be equivalent for each genotype, however, that is not the case. Developing genotypes with high product conversion rate is an important breeding goal in cassava as it drives the adoption rates of new varieties. The objective of this study was to quantify the contribution of genetic and genotype-by-environment interaction (GEI) patterns on cassava root conversion rate to gari and fufu. Sixty-seven advanced breeding genotypes from the International Institute of Tropical Agriculture (IITA) were evaluated across eight environments in Nigeria. Root conversion rate means across trials ranges from 14.72 to 22.76% for gari% and 16.96–24.24% for fufu%. Heritability estimates range from 0.17 to 0.74 for trial bases and 0.71 overall environment for gari% and 0.03–0.65 for trial bases and 0.72 overall environment for fufu% which implies that genetic improvement can be made on these traits. Root conversion rate for both gari and fufu% showed a negative but insignificant correlation with fresh root yield and significant positive correlation to Dry Matter content. For all fitted models, environment and interaction had explained more of the phenotypic variation observed among genotypes for both product conversion rates showing the presence of a strong GEI. Wrickle ecovalence (Wi) stability analysis and Geometric Adaptability index (GAI) identified G40 (TMS14F1285P0006) as part of top 5 genotypes for gari% but no overlapping genotype was identified by both stability analysis for fufu%. This genotypic performance across environments suggests that it is possible to have genotype with dual-purpose for high gari and fufu conversion rate.

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 strategies.