TY  - CONF
AU  - Ćeran, Marina
AU  - Vollmann, Johann
AU  - Ciuca, Matilda
AU  - Toncea, Ion
AU  - Pachner, Martin
AU  - Đorđević, Vuk
PY  - 2024
UR  - http://fiver.ifvcns.rs/handle/123456789/4561
AB  - Marker-assisted selection (MAS) is a valuable tool in crop breeding, including organic breeding. The application of molecular markers allows rapid screening at early developmental stages as well as more accurate and efficient identification and selection of plants with desired traits. Besides accelerating breeding, this approach reduces the need for extensive field trials and multiple generations of selection. Considering that organic breeding emphasizes sustainable practices and minimal environmental impact, MAS can reduce the need for excessive land, water, and resources typically required for conventional breeding. In addition, it can help breeders to develop organic-compliant varieties by targeting specific traits and preserving organic standards, providing more efficient and sustainable strategies for organic production systems. Molecular markers were used to screen ECOBREED soybean germplasm harbouring traits relevant to organic farming, such as cadmium accumulation, supernodulation and disease tolerance to Sclerotinia sclerotiorum and Diaporthe complex. Genotypes with low cadmium accumulation should be used in organic food production to improve food safety, while supernodulation in soybean could be an important trait showing potential for increased nitrogen fixation. Molecular screening of genotypes for disease resistance is especially important in organic farming where chemical interventions are limited, offering an efficient and sustainable strategy for organic production.
PB  - Ljubljana : Kmetijski inštitut Slovenije
C3  - Book of Abstracts, ECOBREED Organic Breeding Conference, 17-19 January 2024, Ljubljana, Slovenia
T1  - Marker-assisted selection for soybean organic breeding
EP  - 17
SP  - 17
UR  - https://hdl.handle.net/21.15107/rcub_fiver_4561
ER  - 

@conference{
author = "Ćeran, Marina and Vollmann, Johann and Ciuca, Matilda and Toncea, Ion and Pachner, Martin and Đorđević, Vuk",
year = "2024",
abstract = "Marker-assisted selection (MAS) is a valuable tool in crop breeding, including organic breeding. The application of molecular markers allows rapid screening at early developmental stages as well as more accurate and efficient identification and selection of plants with desired traits. Besides accelerating breeding, this approach reduces the need for extensive field trials and multiple generations of selection. Considering that organic breeding emphasizes sustainable practices and minimal environmental impact, MAS can reduce the need for excessive land, water, and resources typically required for conventional breeding. In addition, it can help breeders to develop organic-compliant varieties by targeting specific traits and preserving organic standards, providing more efficient and sustainable strategies for organic production systems. Molecular markers were used to screen ECOBREED soybean germplasm harbouring traits relevant to organic farming, such as cadmium accumulation, supernodulation and disease tolerance to Sclerotinia sclerotiorum and Diaporthe complex. Genotypes with low cadmium accumulation should be used in organic food production to improve food safety, while supernodulation in soybean could be an important trait showing potential for increased nitrogen fixation. Molecular screening of genotypes for disease resistance is especially important in organic farming where chemical interventions are limited, offering an efficient and sustainable strategy for organic production.",
publisher = "Ljubljana : Kmetijski inštitut Slovenije",
journal = "Book of Abstracts, ECOBREED Organic Breeding Conference, 17-19 January 2024, Ljubljana, Slovenia",
title = "Marker-assisted selection for soybean organic breeding",
pages = "17-17",
url = "https://hdl.handle.net/21.15107/rcub_fiver_4561"
}

Ćeran, M., Vollmann, J., Ciuca, M., Toncea, I., Pachner, M.,& Đorđević, V.. (2024). Marker-assisted selection for soybean organic breeding. in Book of Abstracts, ECOBREED Organic Breeding Conference, 17-19 January 2024, Ljubljana, Slovenia
Ljubljana : Kmetijski inštitut Slovenije., 17-17.
https://hdl.handle.net/21.15107/rcub_fiver_4561

Ćeran M, Vollmann J, Ciuca M, Toncea I, Pachner M, Đorđević V. Marker-assisted selection for soybean organic breeding. in Book of Abstracts, ECOBREED Organic Breeding Conference, 17-19 January 2024, Ljubljana, Slovenia. 2024;:17-17.
https://hdl.handle.net/21.15107/rcub_fiver_4561 .

Ćeran, Marina, Vollmann, Johann, Ciuca, Matilda, Toncea, Ion, Pachner, Martin, Đorđević, Vuk, "Marker-assisted selection for soybean organic breeding" in Book of Abstracts, ECOBREED Organic Breeding Conference, 17-19 January 2024, Ljubljana, Slovenia (2024):17-17,
https://hdl.handle.net/21.15107/rcub_fiver_4561 .

TY  - JOUR
AU  - Vollmann, Johann
AU  - Rischbeck, Pablo
AU  - Pachner, Martin
AU  - Đorđević, Vuk
AU  - Manschadi, Ahmad
PY  - 2022
UR  - http://fiver.ifvcns.rs/handle/123456789/2963
AB  - Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R2 = 0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in highthroughput screening protocols for N fixation in plant breeding programs.
PB  - Elsevier
T2  - Computers and Electronics in Agriculture
T1  - High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing
SP  - 107169
VL  - 199
DO  - 10.1016/j.compag.2022.107169
ER  - 

@article{
author = "Vollmann, Johann and Rischbeck, Pablo and Pachner, Martin and Đorđević, Vuk and Manschadi, Ahmad",
year = "2022",
abstract = "Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R2 = 0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in highthroughput screening protocols for N fixation in plant breeding programs.",
publisher = "Elsevier",
journal = "Computers and Electronics in Agriculture",
title = "High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing",
pages = "107169",
volume = "199",
doi = "10.1016/j.compag.2022.107169"
}

Vollmann, J., Rischbeck, P., Pachner, M., Đorđević, V.,& Manschadi, A.. (2022). High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing. in Computers and Electronics in Agriculture
Elsevier., 199, 107169.
https://doi.org/10.1016/j.compag.2022.107169

Vollmann J, Rischbeck P, Pachner M, Đorđević V, Manschadi A. High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing. in Computers and Electronics in Agriculture. 2022;199:107169.
doi:10.1016/j.compag.2022.107169 .

Vollmann, Johann, Rischbeck, Pablo, Pachner, Martin, Đorđević, Vuk, Manschadi, Ahmad, "High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing" in Computers and Electronics in Agriculture, 199 (2022):107169,
https://doi.org/10.1016/j.compag.2022.107169 . .