Genotype, nitrogen and harvest management effects on switchgrass production

• Main Author: Seepaul, Ramdeo
• Other Authors: Bisoondat Macoon
• Format: Others
• Language: en
• Published: MSSTATE 2014
• Subjects: Plant and Soil Sciences
• Online Access: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10292013-111733/

<p>Use of switchgrass (<i>Panicum virgatum L.</i>) as a forage and feedstock species requires knowledge of fertilizer application rates and harvest timing to optimize yield and quality. Three experiments were conducted at the Brown Loam Branch Experiment Station, Raymond, MS to quantify nitrogen rates, harvest timing, and genotype effects on biomass, nutrient removal, chemical composition and ethanol yield. Dry matter yield varied with N rate, genotype, harvest frequency and timing. Yields among genotypes were: NF/GA992 = NF/GA001 (13.7 Mg ha^-1) > Alamo (11.6 Mg ha^-1) > Cave-in-Rock (6.1 Mg ha^-1). A single (9.5 Mg ha^-1) or two harvests annually (10.3 Mg ha^-1) produced the greatest dry matter yield. As harvest frequency increased from three (7.3Mg ha^-1) to six (5.9 Mg ha^-1) harvests annually, yield decreased. There was an effect of N application on yield, but not at application rates greater than 80 kg ha^-1. Nitrogen did not consistently affect tissue nutrient concentrations but more frequent harvests led to increased nutrient concentration. Nutrient removal responses to N application were mostly similar to the yield responses. Nitrogen use efficiency and recovery declined as N rate increased. Estimated ethanol yield averaged 162 L Mg^-1 for Alamo, NF/GA001 and NF/GA992 . A single (2.4 kL ha^-1) or 2 harvests annually (2.3 kL ha^-1) produced the greatest ethanol production and was correlated with by biomass yield. Nutrient removal, N use efficiency, N recovery and ethanol production were related to biomass yields rather than chemical composition differences. The findings in this dissertation will enable a database on management effects on ethanol yield and composition, enhance current biomass models, facilitate improved management of feedstock production inputs and improve feasibility of alternative fuel development. </p>