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<p>This study evaluated the use of near-infrared spectroscopy (NIRS) to predict the chemical composition of diets consumed by heifers grazing on mixed pastures. A total of 96 diet samples were collected from eight Brown Swiss heifers, dried at 60&#xa0;°C for 48&#xa0;h, and analysed for crude protein (CP), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), and in vitro dry matter digestibility (IVDMD). Samples were scanned using a Unity Scientific near-infrared spectrometer over the 1100–2500&#xa0;nm wavelength range at 1&#xa0;nm resolution. Prediction models were developed using partial least squares regression in UCAL software. Excellent calibration results were obtained for CP and NDF, with determination coefficients (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{R}_{c}^{2}\)</EquationSource> </InlineEquation>) of 0.99 and 0.94, respectively. Ash and ADF showed good predictive accuracy (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{R}_{c}^{2}\)</EquationSource> </InlineEquation> = 0.85 and 0.86), while IVDMD predictions were moderate (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:{R}_{c}^{2}\)</EquationSource> </InlineEquation> = 0.74). These findings demonstrate that NIRS is a rapid, precise, and reliable tool for estimating key nutritional parameters in heifers’ mixed pasture diets, supporting its use for efficient forage quality monitoring.</p>

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NIRS for predicting Brown Swiss heifer diet composition on mixed pastures in the Amazon region

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Flor L. Mejía,
  • Ives Yoplac,
  • Enrique R. Flores,
  • Ysai Paucar,
  • José Américo Saucedo-Uriarte,
  • William Bardales,
  • Hector V. Vasquez,
  • Javier Ñaupari

摘要

This study evaluated the use of near-infrared spectroscopy (NIRS) to predict the chemical composition of diets consumed by heifers grazing on mixed pastures. A total of 96 diet samples were collected from eight Brown Swiss heifers, dried at 60 °C for 48 h, and analysed for crude protein (CP), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), and in vitro dry matter digestibility (IVDMD). Samples were scanned using a Unity Scientific near-infrared spectrometer over the 1100–2500 nm wavelength range at 1 nm resolution. Prediction models were developed using partial least squares regression in UCAL software. Excellent calibration results were obtained for CP and NDF, with determination coefficients ( \(\:{R}_{c}^{2}\) ) of 0.99 and 0.94, respectively. Ash and ADF showed good predictive accuracy ( \(\:{R}_{c}^{2}\) = 0.85 and 0.86), while IVDMD predictions were moderate ( \(\:{R}_{c}^{2}\) = 0.74). These findings demonstrate that NIRS is a rapid, precise, and reliable tool for estimating key nutritional parameters in heifers’ mixed pasture diets, supporting its use for efficient forage quality monitoring.