@article{Sabreen_Saiga_Rahman_2022, title={Screening Forage Grasses with Atomic Absorption Spectrometry, X-ray Fluorescence and X-ray Microanalysis}, volume={19}, url={https://www.banglajol.info/index.php/SJA/article/view/57685}, DOI={10.3329/sja.v19i2.57685}, abstractNote={<p>Breeding cool-season (C3) grasses with higher magnesium (Mg) content is a promising attempt for reducing grass tetany hazard in ruminants. Faster methods for plant mineral analyses could increase the number of individual plants screened for higher Mg content (High-Mg). This study evaluates the effectiveness of energy dispersive X-ray microanalysis (EDX) as well as energy reflectance X-ray spectrometry (XRF) for screening high-Mg grass genotypes. The approach was verified by using two tall fescue cultivars having known differences in magnesium (Mg) content, viz. HiMag (high-Mg cultivar) and Ky-31 (control cultivar). We assumed that cultivars with known variation in Mg concentrations could provide a test for the applicability of the new methodology in finding naturally occurring high and low Mg containing grass genotypes. Plants samples included a population of 8 plants consisting of four harvests for three years and were analyzed for Mg, calcium (Ca), and potassium (K) by EDX and ERF, and data were verified with atomic absorption spectrometry wet (AAS). While observing the frequency distribution for different nutrient concentrations, HiMag tall fescue showed higher Mg and lower K concentrations than that of Ky-31. There was positive linear relationship between AAS and EDX estimated Mg, Ca and K (r = 0.88, 0.62 and 0.89, respectively), indicating close agreement between AAS and EDX estimation. Also, there was a positive linear relationship between AAS and XRF, as the r values were 0.87, 0.65 and 0.88 for Mg, Ca, and K, respectively. The tetany ration was established for EDX and XRF and the results were dependable with wet chemistry.</p> <p><em>SAARC J. Agric., 19(2): 245-256 (2021)</em>      </p>}, number={2}, journal={SAARC Journal of Agriculture}, author={Sabreen, S and Saiga, S and Rahman, MH}, year={2022}, month={Mar.}, pages={245–256} }