Journal of Rangeland Science (JRS) Estimating Nitrogen and Acid Detergent Fiber Contents of Grass Species using Near Infrared Reflectance Spectroscopy (NIRS) Volume 5 (2015) Issue 4, October 2015 2024 01 30 Estimating Nitrogen and Acid Detergent Fiber Contents of Grass Species using Near Infrared Reflectance Spectroscopy (NIRS) EN Hossein Arzani Tehran university 0000-0002-9149-8865 Anvar Sanaei Ph.D. Student of Range Management, Faculty of Natural Resources, University of Tehran, Iran Alen V. Barker Stockbridge School of Agriculture, University of Massachusetts, Amherst, USA Sahar Ghafari Ph.D. Student of Range Management, Faculty of Natural Resources, University of Ardabil, Iran Javad Motamedi Associate Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran Journal Article 2024 01 30 Chemical assessments of forage clearly determine the forage quality; however, traditional methods of analysis are somehow time consuming, costly, and technically demanding. Near Infrared Reflectance Spectroscopy (NIRS) has been reported as a method for evaluating chemical composition of agriculture products, food, and forage and has several advantages over chemical analyses such as conducting cost-effective and rapid analyses with non-destructive sampling and small number of samples. This study aims to estimate Nitrogen (N) and Acid Detergent Fiber (ADF) content of grass species using NIRS.A total of 171 samples of grasses (Poaceae) at vegetative, flowering, and seeding stages were collected from different regions in Iran. The samples were scanned in a NIRS DA 7200 (Perten instruments, Sweden) in a range of 950-1650 nm. The sample set consisted of 110 samples for calibration and 61 samples for validation was used to predict N and ADF. Samples were previously analyzed chemically for Nitrogen (N) and Acid Detergent Fiber (ADF) and then were scanned by NIRS. Calibration models between chemical data and NIRS were developed using partial least squares regression with the internal cross validation. The coefficients of determination (r2) of linear regression between chemical analyses and NIRS were 0.90 and 0.94 for N and ADF, respectively. The standard errors of prediction were 0.30% and 3.10% for N and ADF, respectively. The results achieved from this study indicated that NIRS has a potential to be used in the measurement of N and ADF contents regarding the forage samples.