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Enregistrement W2784757425 · doi:10.15414/afz.2017.20.04.95-98

Fatty acid composition of maize silages from different hybrids

2017· article· en· W2784757425 sur OpenAlexaboutno aff
Miroslav Juráček, Dániel Bíró, Milan Šimko, Branislav Gálik, Michal Rolinec, Ondrej Hanušovský, Ondrej Pastierik, Adriana Píšová, Norbert Andruška

Notice bibliographique

RevueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAgricultural and Food Production Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSilagePalmitic acidHybridLinoleic acidFatty acidOleic acidFood scienceDry matterPolyunsaturated fatty acidChemistryAgronomyForageBiologyAnimal scienceBiochemistry

Résumé

récupéré en direct d'OpenAlex

Received: 2016-12-13 | Accepted: 2016-12-18 | Available online: 2017-12-31 http://dx.doi.org/10.15414/afz.2017.20.04.95-98 The aim of this research was to determine the fatty acid content in maize silages of different hybrids. Grain hybrid with FAO number 420 and silage hybrid with stay-green maturation with FAO number 450 were evaluated. Maize hybrids were grown under the same agro-ecological conditions, and harvested on growing degree days 1277 (FAO 420) and 1297 (FAO 450). Whole-plant maize was chopped to 10 mm by harvester with kernel processor and immediately ensiled in plastic barrels (volume 50 dm 3 ). Maize matter was ensiled without silage additives. For fatty acids analyses samples of maize silages were taken after 8 week of ensiling. Content of fatty acids was quantified by gas chromatography. Examined maize of both hybrids had the highest linoleic acid content, followed by oleic acid and third highest content of palmitic acid. The results confirmed differences in fatty acid content in maize silages of different hybrids. In silages of grain hybrid was detected significantly higher content of palmitic acid and cis-11-eicosenoic acid and significantly lower content of oleic acid in compared with silage of silage hybrid. This ultimately resulted in a higher polyunsaturated fatty acids content (P < 0.05) in maize silage from grain hybrid and lower monounsaturated fatty acids content (P < 0.05) in maize silage from stay green hybrid.  Keywords: fatty acid, maize, hybrid, silage References Alezones, J. et al. (2010) Caracterización del perfil de ácidos grasos en granos de híbridosde maíz blanco cultivados en Venezuela. Archivos Latinoamericanos de Nutricion , vol. 60, no. 4, pp. 397–404. Alves, S.P. et al. (2011) Effect of ensiling and silage additives on fatty acid composition of ryegrass and corn experimental silages. Journal of Animal Science , vol. 89, no. 8, pp. 2537–2545. doi: https://dx.doi.org/10.2527/jas.2010-3128 Arvidsson, K., Gustavsson, A.-M. and Martinsson, K. (2009) Effects of conservation method on fatty acid composition of silage. Animal Feed Science and Technology , vol. 148, no. 2–4, pp. 241–252. http://dx.doi.org/10.1016/j.anifeedsci.2008.04.003 Balušíková, Ľ. et al. (2017) Fatty acids of maize silages of different hybrids. In NutriNet 2017 . České Budějovice: University of South Bohemia in České Budějovice, pp. 13–19. Bíro, D. et al. (2014) Conservation and adjustment of feeds. Nitra: Slovak University of Agriculture. 223 p. (in Slovak). Blažková, K. et al. (2012) Comparison of in vivo and in vitro digestibility in horses. In Koně 2012 . České Budějovice: University of South Bohemia in České Budějovice, pp. 1–7. Boufaïed, H. et al. (2003) Fatty acids in forages. I. Factors affecting concentrations. Canadian Journal of Animal Science , vol. 83, no. 3, pp. 501–511. doi: http://dx.doi.org/10.4141/a02-098 Capraro, D. et al. (2017) Feeding finishing heavy pigs with corn silages: effects on backfat fatty acid composition and ham weight losses during seasoning. Italian Journal of Animal Science , vol.16, no. 4, pp. 588–592. doi: http://dx.doi.org/10.1080/1828051x.2017.1302825 Commission Regulation (EC) No 152/2009 of 27 January 2009 laying down the methods of sampling and analysis for the official control of feed. L 54/1. 130 p. Eurostat 1 Green maize by area, production and humidity. [Online] Available from: http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&language=en&pcode=tag00101&plugin=1 [Accessed: 2017- 10-30]. Galassi, G. et al. (2016) Digestibility, metabolic utilisation and effects on growth and slaughter traits of diets containing whole plant maize silage in heavy pigs. Italian Journal of Animal Science , vol. 16, no. 1, pp. 122–131. doi: http://dx.doi.org/10.1080/1828051x.2016.1269299 Glasser, E. et al. (2013) Fat and fatty acid content and composition of forages: a meta-analysis. Animal Feed Science and Technology , vol.185, no. 1–2, pp. 19–34. doi: http://dx.doi.org/10.1016/j.anifeedsci.2013.06.010 Guermah, H., Maertens, L. and Berchiche, M. (2016) Nutritive value of brewersʼ grain and maize silage for fattening rabbits. World Rabbit Science , vol. 24, no. 3, pp. 183–189. doi: http://dx.doi.org/10.4995/wrs.2016.4353 Han, L. and Zhou, H. (2013) Effects of ensiling process and antioxidants on fatty acids concentrations and compositions in corn silages. Journal of Animal Science and Biotechnology , vol. 4, no. 1, pp. 1–7. doi: http://dx.doi.org/10.1186%2f2049-1891-4-48 Kalač, P. and Samková, E. (2010) The effects of feeding various forages on fatty acid composition of bovine milk fat: A review. Czech Journal of Animal Science , vol. 55, no. 12, pp. 521–537. Khan, N.A., Cone, J.W. and Hendriks, W.H. (2009) Stability of fatty acids in grass and maize silages after exposure to air during the feed out period. Animal Feed Science and Technology , vol. 154, no. 3–4, pp. 183–192. doi: http://dx.doi.org/10.1016/j.anifeedsci.2009.09.005 Khan, N.A. et al. (2011) Changes in fatty acid content and composition in silage maize during grain filling. Journal of Science of Food and Agriculture , vol. 91, no.6, pp. 1041–1049. doi: http://dx.doi.org/10.1002/jsfa.4279 Khan, N.A. et al. (2012) Causes of variation in fatty acid content and composition in grass and maize silages. Animal Feed Science and Technology , vol. 174, no. 1–2, pp. 36–45. doi: http://dx.doi.org/10.1016/j.anifeedsci.2012.02.006 KHAN, N.A. et al. (2015) Effect of species and harvest maturity on the fatty acids profile of tropical forages. The Journal of Animal & Plant Sciences , vol. 25, no. 3, pp. 739–746. Kokoszyński, D. et al. (2014) Effect of corn silage and quantitative feed restriction on growth performance, body measurements, and carcass tissue composition in White Kołuda W31 geese. Poultry Science ,   vol. 93, no. 8, pp.1993–1999. doi: http://dx.doi.org/10.3382/ps.2013-03833 Loučka, R. and Tyrolová, Y. (2013) Good practice for maize silaging . Praha: Institute of Animal Science. Mir, P.S. (2004) Fats in Corn Silage. Advanced Silage Corn Management 2004. [Online] Available from: http://www.farmwest.com/chapter-8-quality-of-corn-silage [Accessed: 2017- 10-30]. Mojica-Rodríguez, J.E. et al. (2017) Effect of stage of maturity on fatty acid profile in tropical grasses. Corpoica Ciencia Tecnología Agropecuaria , vol. 18, no.2, pp. 217–232. doi: http://dx.doi.org/10.21930/rcta.vol18_num2_art:623 Nazir, N.A. et al. (2011) Changes in fatty acid content and composition in silage maize during grain filling. Journal of the Science of Food and Agriculture , vol. 91, no. 6, pp.1041–1049. http://dx.doi.org/10.1002/jsfa.4279 Oliveira, M.A. et al. (2012) Fatty acids profile of milk from cows fed different maize silage levels and extruded soybeans. Revista Brasileira de Saúde e Produção Animal , vol. 13, no. 1, pp. 192–203. doi:  http://dx.doi.org/10.1590/s1519-99402012000100017 SAS Institute (2008) Statistical Analysis System Institute, Version 9.2 . SAS Institute, Cary, NC, USA. Van Ranst, G. et al. (2009) Influence of herbage species, cultivar and cutting date on fatty acid composition of herbage and lipid metabolism during ensiling. Grass and Forage Science , vol. 64, no. 2, pp. 196–207. doi: http://dx.doi.org/10.1111/j.1365-2494.2009.00686.x Zeman, L. et al. (2006) Nutrition and feeding of livestock . Praha: Profi Press. 360 p. (in Czech).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Science ouverte, Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,308
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,002
Communication savante0,0010,002
Science ouverte0,0060,005
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,263
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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