Management and environmental factors explaining soybean seed protein variability in central Argentina
Soybean production is challenged to increase yield while maintaining seed protein concentration levels. Variability in protein concentration has been poorly described in many regions, and management options targeting their levels are not available. Our objectives were (i) to describe soybean seed pr...
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2019bosaz.pdf LINK AL EDITOR |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 05484nab a22004097a 4500 | ||
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| 001 | 20191022140909.0 | ||
| 003 | AR-BaUFA | ||
| 005 | 20221101134838.0 | ||
| 008 | 191022t2019 ne b||||o|||| 00| | eng d | ||
| 999 | |c 47671 |d 47671 | ||
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| 022 | |a 0378-4290 | ||
| 024 | |a 10.1016/j.fcr.2019.05.007 | ||
| 040 | |a AR-BaUFA | ||
| 245 | 1 | 0 | |a Management and environmental factors explaining soybean seed protein variability in central Argentina |
| 520 | |a Soybean production is challenged to increase yield while maintaining seed protein concentration levels. Variability in protein concentration has been poorly described in many regions, and management options targeting their levels are not available. Our objectives were (i) to describe soybean seed protein in the central production systems of Argentina, (ii) to explore spatial patterns across the region, (iii) to quantify the importance of management and environment on protein concentration, and (iv) to explore correlations between seed protein concentration, seed yield, and total canopy N uptake. We sampled 1721 farm fields across the region, and conducted 52 cultivar trials from 2012 to 2016. Average seed protein concentration was 36.6 and 37.6% for soybean grown as a single crop in the season and as a second crop after a winter one, respectively. Seed protein concentration showed a spatial pattern in soybean as single crop, allowing us to identify well delimited areas with contrasting protein values. Regression trees of farm fields explained 38 and 48% of total variation in seed protein for soybean as single and second crop, respectively. Relative to this explained variation, management variables accounted for ∼73% of seed protein concentration variation in both crops, where cultivar selection was the most relevant (71.5 and 68.9% for soybean as single and second crop, respectively), followed by planting date (2.5%), and maturity group (1.8%). Despite its minor importance compared to management practices, temperature and rainfall were also associated to protein variability. The response to temperature differed between soybeans as single and second crop. Reduced rainfall was associated to increased seed protein concentration only for soybean as a second crop. Cultivar trials identified consistent high seed protein cultivars. Sites with high yield and quality were evident, but results showed clear cultivar trade-off correlations between yield and protein concentration. Results highlight the need to investigate ways to improve total canopy N uptake for allowing both yield and seed protein to increase simultaneously. | ||
| 653 | |a GLYCINE MAX (L) MERR. | ||
| 653 | |a KRIGING | ||
| 653 | |a REGRESSION TREE | ||
| 653 | |a SEED COMPOSITION | ||
| 653 | |a SPATIAL VARIABILITY | ||
| 700 | 1 | |a Bosaz, Lina B. |u Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |u CONICET - Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |9 69455 | |
| 700 | 1 | |a Gerde, José A. |u Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |u CONICET - Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |9 69456 | |
| 700 | 1 | |9 11393 |a Borrás, Lucas |u Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |u CONICET - Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. | |
| 700 | 1 | |9 20940 |a Cipriotti, Pablo Ariel |u Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información. Buenos Aires, Argentina. |u Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. |u CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. | |
| 700 | 1 | |a Ascheri, Luciano M. |u Asociación Argentina de Consorcios Regionales de Experimentación Agrícola. Sarmiento. Buenos Aires, Argentina. |9 40842 | |
| 700 | 1 | |a Campos, Matías |u Asociación Argentina de Consorcios Regionales de Experimentación Agrícola. Sarmiento. Buenos Aires, Argentina. |9 69458 | |
| 700 | 1 | |a Gallo, Santiago |u Asociación Argentina de Consorcios Regionales de Experimentación Agrícola. Sarmiento. Buenos Aires, Argentina. |9 67997 | |
| 700 | 1 | |9 16934 |a Rotundo, José Luis |u Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. |u CONICET - Universidad Nacional de Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Rosario, Santa Fe, Argentina. | |
| 773 | 0 | |t Field crops research |w (AR-BaUFA)SECS000083 |g vol.240 (2019), p.34–43, tbls., grafs., mapas | |
| 856 | |f 2019bosaz |i en reservorio |q application/pdf |u http://ri.agro.uba.ar/files/intranet/articulo/2019bosaz.pdf |x ARTI201911 | ||
| 856 | |z LINK AL EDITOR |u https://www.elsevier.com | ||
| 942 | 0 | |c ARTICULO | |
| 942 | 0 | |c ENLINEA | |
| 976 | |a AAG | ||