The study unrolled in South Kivu province situated in the East of DRC, in the North part of Kabare territory, respectively on-farm PABU/Katana and on-station INERA/Mulungu. This study aims to identify among the performing varieties the ones which better fit and witch have the high potentially seeds yields and stables in these sites. The comparative adaptation trials have been lead according to the layout in randomized completed block design with three replications and the results analysis was done by using software Statistix 8 and Genstat. The results (at0.05) show that eight varieties got global medium yields significantly over the check VCB81013 (1454kg/ha) in the two sites during the four farming seasons 2015-16AB. It concerns the varieties NUV234-3-1(1932,5 kg/ha), NUV108-3-1(1764,5), NUV108-3-2 (1578,5), NUV119-3 (1506), NUV234-3-2 (1719,5), NUV15-1 (1711), NUV15-2 (1620) and NUV131-1 (1598,5). These varieties have given the best yields exceeding 1500kg/ha with 132,8 à 103,5% upper to the check. As regards to varieties sensibility, the results of the global statistical analysis showed a resistant reaction of the majority of all varieties to the main diseases (1 to 3), at the standard scale of diseases evaluation (1 to 9) regard to CIAT.

The aim of the study was to determine variation in iron and zinc concentrations in farmer’s accessions, relationship, and rates between the two essential minerals. Six sites were utilized for collecting accessions in two bean growing zones: South and North Kivu. The South Kivu comprised: Kabare, Walungu and Uvira and the North Kivu: sites of Goma, Rutshuru and Butembo. The CIAT Atlas “African Bean Environment (AFBE) was utilized to characterize the sites. Thousand collected accessions were characterized and went seed increase. The mineral analysis was carried out at Adailine Laboratory, Australia using the inductively coupled plasma (ICP)-method. The 383 genotypes selected went statistical analyses. The ANOVA and correlation analysis was performed using Pearson’s product-moment method to determine the relationship between iron and zinc concentrations. The rates and trends of iron and zinc concentrations were calculated using respectively R software Analysis and the Scatter Diagram Technique. All data were analyzed using Statistix10 and R Development Core Team Software. High variation in iron and zinc concentrations with mean rages of 71.63 ppm for iron and 30.69 ppm for zinc was found. Highly significant correlation (r=0.94***) between iron and zinc concentrations. The effects of variety, environment and growth habit on the mineral concentrations of genotypes were highly significant (***). The rates calculated based on targets were: 3.4% at low concentration (40-50 ppm); 21.7% at baseline level (50-60ppm); 31.0% at increment target (60-70ppm); 21.4% at 1st wave target (70-80ppm); at 2nd wave target (80-90 ppm); and 3rd wave target ≤94 ppm).