2.3.1 Increasing Biomass Collection Efficiency by Responding to In-Field Variability
Collection efficiency (the ratio of biomass collected to the total amount available in the field) ranked second in sensitivity, fifth in uncertainty, second in influence, and highest in overall combined ranking. The probability distribution for collection efficiency used in this analysis was based on a review of reported corn stover collection efficiencies (Richey et al., 1982; Shinners et al., 2003; Schechninger and Hettenhaus, 2004; Shinners and Binversie, 2004; Prewitt et al., 2007) from which we chose a most likely value of 43%, a minimum of 19%, and a maximum of 65%. The wide range of reported results show that current machinery itself is capable of high removal
0 Removal fraction (%)
25 30 35 40 45 50 55 60 65 70 75 80
FIGURE 2.7 Sustainable subfield residue harvest plan that varies the removal rate between 0% and 80% (5.6 Mg/ha).
rates, but sustainability (Wilhelm et al., 2010) and quality (Prewitt et al., 2007) constraints often dictate deliberately conservative collection efficiencies. Therefore, sustainability and quality constraints are two main sources of uncertainty relating to corn stover collection efficiencies, and reducing feedstock cost involves solving uncertainty around these two issues.
It has generally been reported that corn stover removal rates of 30–40% could be sustainable over most corn acres (Nelson et al., 2004; Gregg and Izaurralde, 2010, Perlack et al., 2011). However, corn stover removal at a rate of 30–40% is often not economically viable. The emerging biorefining industry has estimated minimum removal rates of two dry short tons (DST) per acre for system economics to support corn stover removal operations.
Two management strategies have emerged to deal with this challenge and sus- tainably achieve a two DST/acre removal rate. The first is an equipment develop- ment strategy—variable rate residue harvesting—and the second is an agronomic strategy—implementing interval removal schemas (Muth and Bryden 2012). Mul- tifactor sustainability analyses (Figure 2.7) have shown that advanced variable-rate harvesting systems capable of responding to subfield variability in topography, soil characteristics, and grain yield could achieve average removal rates as high as 75%
(7.69 Mg/ha) without violating sustainability requirements (Muth and Bryden, 2012).
Interval removal schemas allow conventional equipment to be used to collect corn stover one out of every 2 or 3 years, based on soil erosion and soil organic carbon constraints for an individual field.
Our own testing has shown that removal rates as high as 80% are attainable with wheel rake, flail shredder, or bar rake windrowers; however, increasing corn stover collection efficiencies with conventional harvest systems tends to reduce stover
16 Stalk chopper Bar rake Wheel rake 15
14 13 12
Ash, (wt. %)
11 10 9 8 7 6
High removal Low removal
FIGURE 2.8 Corn stover large square bale ash content variability due to windrowing machin- ery and removal rate.
quality by increasing the ash content (Figure 2.8). The increase in stover ash content—
both between equipment and between removal rates—is largely attributed to increased soil entrainment (Prewitt et al., 2007), but may also be attributed to differences in anatomical composition (Hoskinson et al., 2007). Though the dataset is too limited to support specific solutions, it shows that quality is a variable affecting collection efficiency uncertainty, and a priority on low ash content generally necessitates reduced collection efficiencies. The data also show that an equipment development solution that eliminates a variable from the uncertainty equation, such as a bar rake in this case, is beneficial. Like the sustainability problem, potential solutions exist that include both conventional and advanced harvesting systems. Understanding variable field factors that affect susceptibility of soil to disturbance and entrainment during windrowing may provide a solution for selection and configuration of conventional harvest equipment that can maintain ash content at acceptable levels regardless of removal rate. Ultimately, single-pass harvest systems that eliminate biomass/ground contact during harvest will provide the best opportunity to remove the quality variable affecting collection efficiencies.
2.3.2 Minimizing Storage Losses by Addressing Moisture Variability Storage dry matter losses (loss of structural carbohydrates, water-soluble compo- nents, lignin, and ash resulting from biological deterioration and/or physical losses in storage) ranked fourteenth in sensitivity, first in uncertainty, twelfth in influence, and eighth in overall combined ranking. The probability distribution for dry matter loss used in this analysis was based on a review (Coble and Egg, 1987; Sanderson et al., 1997; Shinners et al., 2007; Shinners et al., 2010) of reported dry matter loss for dry (<20%, wet basis) aerobic storage, from which we chose a most likely value of 5%, a minimum of 1%, and a maximum of 8%. The range selected actually underestimates
4 0 10 Frequency 20
30 40 50 60
2009 harvest 2010 harvest
8 12 16 20 24 28
% moisture (wet basis)
32 36 40 44 48
FIGURE 2.9 Midwest corn stover harvested in 2009 exceeded moisture limits for stable bale storage, while in 2010 nearly all harvested biomass could be stored stable in conventional stacks.
the true variability and uncertainty around storage dry matter losses. Storage losses can vary significantly depending on bale moisture, storage method, and environmen- tal conditions during storage; these variables make control and prediction of dry matter loss in storage highly uncertain (Shinners et al., 2007; Darr and Shah, 2012).
Controlling dry matter losses and reducing uncertainty is highly dependent on moisture management. The ability to field-dry to safe aerobic storage conditions is compromised by often poor, always uncertain, field-drying conditions following the fall grain harvest. This is clearly demonstrated in Midwest corn stover moistures during the 2009 and 2010 feed corn harvest seasons (Figure 2.9). This annual vari- ability in biomass moisture content presents significant challenges in implementing consistent and reliable storage practices.
Proper moisture management does not end in the field. Even with ideal agronomic conditions where the material dries down to safe aerobic storage levels before baling, other often subtle differences in storage methods can significantly affect dry matter losses. For example, the orientation of stacks as well as the characteristics of the ground surface on which bales are stacked can greatly affect storage conditions. In a corn stover storage study, we found bales to be more moist on the north-facing side than the south exhibiting up to 40% moisture (wb) differences from side to side (Figure 2.10). Bottom bales also accumulated more moisture, even when placed on crushed gravel, which was attributed to water running off the tarp and draining toward the stack (Smith et al., 2013). Thus, bale storage is a dynamic practice with no one-size-fits-all solution. Bales are capable of long-term storage with a proper understanding of the material’s condition (moisture content at time of receipt and any past storage information) provided that they are properly managed. The key factors to control are the accumulation of moisture and the timing of this accumulation with respect to environmental conditions in storage.
70
60
50 10
20
FIGURE 2.10 Isopleth moisture distribution of an end view of a stack of 3×4×8-ft large square bales (stacked 1-bale wide×4 bales high) showing the influence of Northern exposure on bale moisture. (For a color version, see the color plate section.)