Genotypic variability and genetic parameters for root yield, dry matter and related traits of cassava in the Guinea Savannah ecological zone of Ghana

The aim of this study was to assess the agronomic performance and genetic parameters governing storage root yield and related traits in cassava genotypes in order to identify superior genotypes. The study involved 18 elite cassava genotypes which were arranged in a randomized complete block design with three replications and assessed for storage root yield and yield components (12 months after planting) in the Guinea savannah ecology of Ghana for three seasons. Analysis of variance indicated significant (p < 0.05) genotype and year main effects for fresh and dry root yields, dry matter content, starch yield and harvest index. Genotype × year effect was significant (p < 0.05) for fresh root yield, dry root yield and starch yield. Estimates of the variance components revealed greater genotypic influence for starch yield, fresh and dry root yields implying the potential for genetic gain with selection for these traits among the genotypes. Relatively high (69 %) broad sense heritability estimate was observed for dry storage root yield indicating the depth of genetic influence. Path coefficient analysis revealed a direct positive effect of dry matter content on dry storage root yield whilst dry storage root yield had direct positive effect on starch yield suggesting the possibility of indirect selection for starch yield through dry storage root yield. The study revealed ample genotypic variability among the cassava genotypes to warrant selection. Four genotypes, IBA 070134, IBA 419, IBA 950289 and IBA 980581 were identified for high and stable fresh and dry root yields for multilocational testing

High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana

In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered