Prediction models of the nutritional quality of fresh and dry Brachiaria brizantha cv. Piatã grass by near infrared spectroscopy

Autor: Mariellen Cristine Andrade Ribeiro, Geisi Loures Guerra, Camila Cano Serafim, Larissa Nóbrega de Carvalho, Sandra Galbeiro, Pedro Rodolfo Siqueira Vendrame, João Pedro Monteiro do Carmo, Erica Regina Rodrigues Franconere, Jéssica Geralda Ferracini, Ivanor Nunes do Prado, Odimári Pricila Prado Calixto, Ivone Yurika Mizubuti
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Applied Animal Research, Vol 51, Iss 1, Pp 193-203 (2023)
Druh dokumentu: article
ISSN: 09712119
0974-1844
0971-2119
DOI: 10.1080/09712119.2023.2172022
Popis: This study aimed to generate prediction models to estimate the chemical composition of fresh and dry Brachiaria brizantha cv. Piatã grass using near infrared spectroscopy (NIRS). Chemical analyses of 249 samples were performed to determine oven-dried sample (ODS), dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose (CEL) and total digestible nutrients (TDN). The samples were scanned in an NIRS spectrometer and different percentages were used to compose and develop the models (100% fresh; 100% dry; 25% fresh:75% dry; 50% fresh:50% dry and 75% fresh:25% dry). The purpose of these mixed models is to know if it is possible to obtain reliable predictions from fresh samples in a database that contains dry samples. The calibration models were developed using modified partial least squares (MPLS) and evaluated by statistical parameters, including coefficient of determination (R²) and residual predictive deviation (RPD). The model with 100% dry samples obtained the best results in R² and RPD validations, for CP (0.94; 3.98), NDF (0.92; 3,49) and TDN (0.90; 3.12). The 100% fresh samples produced the best R² results in ODS (0.83), CP (0.85), ADF (0.84) and ADL (0.83). A screening model was validated to predict the characteristics and components of the fresh samples. The model using 100% dry grass was suitable for predicting all the variables, except ODS, DM and CEL.HighlightsPrediction models can be used for assessment of fresh forage, allowing producers to make quicker decisions, thereby saving time and money.
Databáze: Directory of Open Access Journals
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