| Switchgrass (Panicum virgatum L.) is considered a cellulosic feedstock suitable for biofuel production because of its drought tolerance, noninvasiveness, potential for soil carbon sequestration, and high biomass yield with low inputs. Information is needed on seasonal trends in biomass accumulation and nutrient removal to support decision-making on timing of harvest to maximize efficiency of fertilizer nutrient use. The principal aim of this study was to characterize seasonal trends in biomass yield, moisture content, macronutrient removal, ash concentration, gross energy density, leaf area index (LAI), and light interception. The secondary aim of this study was to calibrate and verify the ALMANAC model for predicting biomass yields in Arkansas. Plots were established in 2008 in Fayetteville, AR, with cv. Alamo in six replications. Plots were randomly assigned dates for harvest and were sampled approximately monthly from May 2009 to February 2010. Regressions were fitted to the data to describe the trends. Switchgrass dry weight yields increased in a sigmoidal pattern starting at 0.18 Mg ha-1on Day 121(1 May) and approached the asymptote of 13.2 Mg ha-1 in late August (Day 240). Yields decreased almost 26% post-senescence; however, tissue moisture content decreased to low enough levels for harvesting directly into storage (≤ 200 g kg-1, wet weight basis) during mid-December. Likewise, mineral element concentrations and gross energy density generally decreased with date of harvest until February. Ash concentration decreased with date of harvest (73.5 to 15.1 g kg-1 from May to February). Potassium uptake peaked on 3 July at 136.2 kg ha-1 then declined, whereas nitrogen and phosphorus uptake tended to peak later in the season (28 August) at 79.9 kg ha-1 and 15.7 kg ha-1, respectively. Light interception approached an asymptote of 96% towards Day 240 (end of August) during the flowering and seed filling period, whereas LAI began to decline after Day 212 (mid-June). Once calibrated, ALMANAC predicted yields acceptably well, as judged by simulated yields falling within one standard deviation of the measured yields. Necessary inputs for model accuracy were: localized weather data, subsurface flow, and other site-specific model values, such as potential heat units. More refinement of the model is needed over a wider range of soils and climatic conditions. These results may feed into a decision support system for estimating biomass harvest yields and thus timing harvests for optimal storage and shipping logistics. Additionally, timing of harvest and other management decisions dictate the feedstock composition and quality, the conversion system employed, and ultimately, the overall feasibility of feedstock production. |
