Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by near-infrared reflectance spectroscopy (NIRS). | Turkish Journal of Agriculture and Forestry Turk J Agric For (2016) 40: 45-52 © TÜBİTAK doi: Research Article Determination of forage quality by near-infrared reflectance spectroscopy in soybean 1 2 3 3 1 Sovetgul ASEKOVA , Sang-Ik HAN , Hong-Jib CHOI , Sang-Jo PARK , Dong-Hyun SHIN , 4 5 1, Chan-Ho KWON , J. Grover SHANNON , Jeong-Dong LEE * 1 School of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea 2 Department of Functional Crops, Functional Crop Resource Development Division, RDA, Milyang, Republic of Korea 3 Gyeongsangbuk-do Agricultural Research and Extension Services, Daegu, Republic of Korea 4 College of Animal Science, Department of Horse/Companion and Wild Animals, Kyungpook National University, Sangju, Republic of Korea 5 Division of Plant Sciences, University of Missouri Delta Center, Portageville, MO, USA Received: Accepted/Published Online: Final Version: Abstract: Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by near-infrared reflectance spectroscopy (NIRS). In this study, 353 forage soybean samples were used to develop near-infrared reflectance (NIR) equations to estimate four forage quality parameters: crude protein (CP), crude fat (CF), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Samples included 181 recombinant inbred lines derived from PI 483463 (G. soja) × Hutcheson (G. max), 104 cultivated soybeans (G. max), and 68 wild soybeans (G. soja). Two NIR equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in the external validation set (r2) were for CF, for CP, for NDF, and for ADF. The relative predictive .
