Predição Nutricional de Grãos de Milho: Integração de Genótipos e Dados Meteorológicos para Suinocultura

Murilo Vieira Loro; Alberto Cargnelutti Filho

doi:10.1590/1809-6891v26e-81440E

Authors

Murilo Vieira Loro Universidade Federal de Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil https://orcid.org/0000-0003-0241-4226
Alberto Cargnelutti Filho Universidade Federal de Santa Maria (UFSM), Santa Maria, Rio Grande do Sul, Brazil https://orcid.org/0000-0002-8608-9960

DOI:

https://doi.org/10.1590/1809-6891v26e-81440E

Abstract

In this study, the aim was to evaluate whether there is variability in the protein nutritional composition of grains between maize genetic bases (simple hybrids, triple hybrids, double hybrids and varieties) and sowing dates, and predict digestible amino acids for pigs based on crude protein and meteorological variables. 773 grain samples from four maize genetic bases cultivated on ten sowing dates were evaluated using Near Infrared Reflectance Spectroscopy. Simple linear regressions were performed for protein nutritional traits in different genetic backgrounds and sowing dates. Principal component analysis was used to group data on the protein nutritional composition of grains and meteorological variables, by genetic bases and sowing dates. There is variation in the digestible levels of the eleven amino acids in grains between maize genetic bases and sowing dates. Maize varieties have the highest digestible levels of eleven amino acids in the grains, compared to simple hybrids, triple hybrids and double hybrids, regardless of the sowing date. Sowings carried out in October and November show higher digestible levels of the eleven amino acids in maize grains, in relation to sowings in the months of September, December, January and February, regardless of the genetic basis. The digestible contents of methionine, cystine, threonine, valine, isoleucine, leucine, phenylalanine, histidine and arginine in maize kernels can be predicted from crude protein with high accuracy, on all genetic bases.

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References

Knudsen KEB. Fiber and nonstarch polysaccharide content and variation in common crops used in broiler diets. Poultry Science. 2014; 93(9):2380-2393. Available from: https://doi.org/10.3382/ps.2014-03902

Simões CT, Vidal JK, Tyska D, Mallmann AO, Madalosso T, Mallmann CA. Assessment of field traits, nutrient composition and digestible amino acids of corns with different endosperm textures for poultry and swine. Animal Feed Science and Technology. 2023;295:115510. Available from: https://doi.org/10.1016/j.anifeedsci.2022.115510

Sriperm N, Pesti GM, Tillman PB. The distribution of crude protein and amino acid content in maize grain and soybean meal. Animal Feed Science and Technology. 2010;159(3-4):131-137. Available from: https://doi.org/10.1016/j.anifeedsci.2010.05.009

Moughan PJ, Deglaire A, Wolfe RR. Amino acid metabolism – an overview. Moughan PJ, Hendriks WH. (Eds.), Feed evaluation science, Wageningen Academic Publishers, The Netherlands. 2018; 219-248.

Millet S, Aluwé M, Van Den Broeke A, Leen F, Boever J, Campeneere S. Pork production with maximal nitrogen efficiency. Animal. 2018;12(5): 1060-1067. Available from: https://doi.org/10.1017/S1751731117002610

Olukosi OA, Adebiyi AO. Chemical composition and prediction of amino acid content of maize-and wheat-distillers’ dried grains with soluble. Animal Feed Science and Technology. 2013;185(3-4):182-189. Available from: https://doi.org/10.1016/j.anifeedsci.2013.08.003

Zuber T, Rodehutscord M. Variability in amino acid digestibility and metabolizable energy of corn studied in cecectomized laying hens. Poultry Science. 2017;96(6):1696-1706. Available from: https://doi.org/10.3382/ps/pew429

Sheikhhasan BS, Moravej H, Ghaziani F, Esteve-Garcia E, Kim WK. Relationship between chemical composition and standardized ileal digestible amino acid contents of corn grain in broiler chickens. Poultry Science. 2020;99(9):4496-4504. Available from: https://doi.org/10.1016/j.psj.2020.06.013

Ebadi MR, Sedghi M, Golian A, Ahmadi H. Prediction of the true digestible amino acid contents from the chemical composition of sorghum grain for poultry. Poultry Science. 2011;90(10): 2397-2401. Available from: https://doi.org/10.3382/ps.2011-01413

Espinosa CD, Fanelli NS, Stein HH. Digestibility of amino acids and concentration of metabolizable energy are greater in high-oil corn than in conventional corn when fed to growing pigs. Animal Feed Science and Technology. 2021;280:115040. Available from: https://doi.org/10.1016/j.anifeedsci.2021.115040

Kil DY, Park CS, Son AR, Ji SY, Kim BG. Digestibility of crude protein and amino acids in corn grains from different origins for pigs. Animal Feed Science and Technology. 2014;196:68-75. Available from: https://doi.org/10.1016/j.anifeedsci.2014.06.008

Opapeju FO, Nyachoti CM, House JD. Digestible energy, protein and amino acid content in selected short season corn cultivars fed to growing pigs. Canadian Journal of Animal Science. 2007;87(2):221-226. Available from: https://doi.org/10.4141/A05-079

Rodriguez DA, Lee SA, Jones CK, Htoo JK, Stein HH. Digestibility of amino acids, fiber, and energy by growing pigs, and concentrations of digestible and metabolizable energy in yellow dent corn, hard red winter wheat, and sorghum may be influenced by extrusion. Animal Feed Science and Technology. 2020;268:114602. Available from: https://doi.org/10.1016/j.anifeedsci.2020.114602

Dong W, Li J, Li Z, Zhang S, Li X, Yang C, Liu L, Zhang S. Physicochemical properties and energy content of yellow dent corn from different climatic origins in growing pigs. Asian-Australasian Journal of Animal Sciences. 2020;33(11):1787. Available from: https://doi.org/10.5713/ajas.19.0715

Guo J, Qu L, Wang L, Lu W, Lu D. Effects of post silking drought stress degree on grain yield and quality of waxy maize. Journal of the Science of Food and Agriculture. 2023;103(3):1530-1540. Disponível em: https://doi.org/10.1002/jsfa.12250

Safian N, Naderi MR, Torabi M, Soleymani A, Salemi HR. Corn (Zea mays L.) and sorghum (Sorghum bicolor (L.) Moench) yield and nutritional quality affected by drought stress. Biocatalysis and Agricultural Biotechnology. 2022;45:102486. Available from: https://doi.org/10.1016/j.bcab.2022.102486

Yang H, Gu X, Ding M, Lu W, Lu D. Heat stress during grain filling affects activities of enzymes involved in grain protein and starch synthesis in waxy maize. Scientific Reports. 2018;8(1):15665. Available from: https://doi.org/10.1038/s41598-018-33644-z

Wang L, Yu X, Gao J, Ma D, Guo H, Hu S. Patterns of influence of meteorological elements on maize grain weight and nutritional quality. Agronomy. 2023;13(2):424. Available from: https://doi.org/10.3390/agronomy13020424

Wu Y, Zhou G, Song X, Song Y, Ren S, Geng J, Zhao H. Key stage and its optimum meteorological conditions affecting the nutritional quality of maize. Agronomy. 2024;14(3): 420. Available from: https://doi.org/10.3390/agronomy14030420

Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Parovek G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift. 2013;22:711-728. Available from: https://doi.org/10.1127/0941-2948/2013/0507

Santos H, Jacomine PKT, Anjos LHC, Oliveira VA, Lumbreras JF, Coelho MR, Almeida JÁ, Araújo Filho JC, Oliveira JB, Cunha TJF. Sistema Brasileiro de Classificação de Solos. 5. ed. Brasília: Embrapa. 2018, 356p. Available from: https://www.infoteca.cnptia.embrapa.br/handle/doc/1094003. Acesso em: 17/12/2024.

Fancelli AL, Dourado Neto D. Milho: manejo e produtividade. Piracicaba: ESALQ/USP. 2009, 181p.

Arnold CY. Maximum-minimum temperatures as a basis for computing heat units. Journal of the American Society for Horticultural Sciences. 1960;76(1):682-692.

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2024. Available from: https://www.R-project.org

Mehta BK, Muthusamy V, Baveja A, Chauhan HS, Chhabra R, Bhatt V, Chand G, Zunjare RU, Singh AK, Hossain F. Composition analysis of lysine, tryptophan and provitamin-A during different stages of kernel development in biofortified sweet corn. Journal of Food Composition and Analysis. 2020;94:103625. Available from: https://doi.org/10.1016/j.jfca.2020.103625

Shrestha V, Yobi A, Slaten ML, Chan YO, Holden S, Gyawali A, Flint-Garcia S, Lipka AE, Angelovici R. Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition. Plant Physiology. 2022;188(1):111-133. Available from: https://doi.org/10.1093/plphys/kiab390

Alves BM, Cargnelutti Filho A. Linear relationships between agronomic and nutritional traits in transgenic genotypes of maize. Journal of Cereal Science. 2017;76:35-41. Available from: https://doi.org/10.1016/j.jcs.2017.05.010

Alves BM, Cargnelutti Filho A, Burin C, Toebe M. Correlações canônicas entre caracteres agronômicos e nutricionais proteicos e energéticos em genótipos de milho. Revista Brasileira de Milho e Sorgo. 2016;15(2):171-185. Available from: https://doi.org/10.18512/1980-6477/rbms.v15n2p171-185

Shewry PR. Improving the protein content and composition of cereal grain. Journal of Cereal Science. 2007;46(3):239-250. Available from: https://doi.org/10.1016/j.jcs.2007.06.006

Rostagno HS, Albino LFT. Tabelas brasileiras para aves e suínos. Composição de alimentos e exigências nutricionais. 5º ed. UFV, Viçosa, MG, Brasil, 2024. 531p.