Predicting timothy mineral concentrations, dietary cation-anion difference, and grass tetany index by near-infrared reflectance spectroscopy

• Published in: Journal of dairy science 2009-09, Vol.92 (9), p.4499-4506
• Main Authors: Tremblay, G.F., Nie, Z., Bélanger, G., Pelletier, S., Allard, G.
• Format: Article
• Language: English
• Subjects: Animal Feed - analysis; Animal productions; Animals; anions; Anions - analysis; Biological and medical sciences; calcium; Calibration; cations; Cations - analysis; Cattle; Cattle Diseases - metabolism; chemical concentration; chlorides; Diet - veterinary; dietary minerals; feed composition; feed rations; Female; Food industries; forage grasses; Fundamental and applied biological sciences. Psychology; grass tetany; hypomagnesemia; magnesium; Milk and cheese industries. Ice creams; milk fever; mineral content; near-infrared reflectance spectroscopy; Parturient Paresis - metabolism; Phleum - chemistry; Phleum pratense; phosphorus; potassium; prediction; Pregnancy; Reproducibility of Results; sodium; Spectroscopy, Near-Infrared; sulfur; Terrestrial animal productions; Tetany - veterinary; Vertebrates
• ISSN: 0022-0302; 1525-3198
• DOI: 10.3168/jds.2008-1973

The mineral concentration of forage grasses plays a significant role in 2 metabolic disorders in dairy cattle production, namely, hypocalcemia (milk fever) and hypomagnesemia (grass tetany). Risks of occurrence of these 2 metabolic disorders can be evaluated by determining the dietary cation-anion difference (DCAD) and the grass tetany (GT) index of forages and specific rations. The objective of this study was to evaluate the feasibility of predicting timothy (Phleum pratense L.) mineral concentrations of Na, K, Ca, Mg, Cl, S, and P, the DCAD, and the GT index by near-infrared reflectance spectroscopy (NIRS). Timothy samples (n = 1,108) were scanned using NIRS and analyzed for the concentration of 7 mineral elements. Calculations of the DCAD were made using 3 different formulas, and the GT index was also calculated. Samples were divided into calibration (n = 240) and validation (n = 868) sets. The calibration, cross-validation, and prediction for mineral concentrations, the DCAD, and the GT index were performed using modified partial least squares regression. Concentrations of K, Ca, Mg, Cl, and P were successfully predicted with coefficients of determination of prediction RP2 of 0.69 to 0.92 and coefficients of variation of prediction (CVP) ranging from 6.6 to 11.4%. The prediction of Na and S concentrations failed, with respective Rp2 of 0.58 and 0.53 and CVP of 82.2 and 12.9%. The 3 calculated DCAD and the GT index were predicted successfully, with RP2 >0.90 and CVP