Volume 25, Issue 1, December 2018, Pages 131–139
N. Mbikayi1, A.D. Mumba2, P.M. Kiman3, R. Kizungu4, and C. Mirindi5
1 Mulungu Research Station, Institut National d'Etude et de Recherche Agronomiques, INERA, P.O. Box 2037 Kinshasa 1, Av. de Cliniques, Kinshasa-Gombe, Bukavu, Sud Kivu, RD Congo
2 Université Pédagogique Nationale (UPN), Département de Phytotechnie, B.P. 8815, Kinshasa-Ngaliema, RD Congo
3 University of Nairobi (OUN), Kenya
4 Université de Kinshasa-11, Faculté d’Agronomie, RD Congo
5 Mulungu Research Station, Institut National d'Etude et de Recherche Agronomiques, INERA, P.O. Box 2037 Kinshasa 1, Av. de Cliniques, Kinshasa-Gombe, Bukavu, Sud Kivu, RD Congo
Original language: English
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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).
Author Keywords: Bean farmer’s accessions, Biofortified beans, Mineral analysis.
N. Mbikayi1, A.D. Mumba2, P.M. Kiman3, R. Kizungu4, and C. Mirindi5
1 Mulungu Research Station, Institut National d'Etude et de Recherche Agronomiques, INERA, P.O. Box 2037 Kinshasa 1, Av. de Cliniques, Kinshasa-Gombe, Bukavu, Sud Kivu, RD Congo
2 Université Pédagogique Nationale (UPN), Département de Phytotechnie, B.P. 8815, Kinshasa-Ngaliema, RD Congo
3 University of Nairobi (OUN), Kenya
4 Université de Kinshasa-11, Faculté d’Agronomie, RD Congo
5 Mulungu Research Station, Institut National d'Etude et de Recherche Agronomiques, INERA, P.O. Box 2037 Kinshasa 1, Av. de Cliniques, Kinshasa-Gombe, Bukavu, Sud Kivu, RD Congo
Original language: English
Copyright © 2018 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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).
Author Keywords: Bean farmer’s accessions, Biofortified beans, Mineral analysis.
How to Cite this Article
N. Mbikayi, A.D. Mumba, P.M. Kiman, R. Kizungu, and C. Mirindi, “Identification of biofortified beans (Phaseolus vulgaris L) : Case study on genetic diversity, relationship and rates of iron and zinc concentrations in farmer’s accession, in eastern DR Congo,” International Journal of Innovation and Applied Studies, vol. 25, no. 1, pp. 131–139, December 2018.
