Comparing Bioenergy Production Sites in the Southeastern US Regarding Ecosystem Service Supply and Demand

<div><p>Biomass for bioenergy is debated for its potential synergies or tradeoffs with other provisioning and regulating ecosystem services (ESS). This biomass may originate from different production systems and may be purposefully grown or obtained from residues. Increased concerns globally about the sustainable production of biomass for bioenergy has resulted in numerous certification schemes focusing on best management practices, mostly operating at the plot/field scale. In this study, we compare the ESS of two watersheds in the southeastern US. We show the ESS tradeoffs and synergies of plantation forestry, i.e., pine poles, and agricultural production, i.e., wheat straw and corn stover, with the counterfactual natural or semi-natural forest in both watersheds. The plantation forestry showed less distinct tradeoffs than did corn and wheat production, i.e., for carbon storage, P and sediment retention, groundwater recharge, and biodiversity. Using indicators of landscape composition and configuration, we showed that landscape planning can affect the overall ESS supply and can partly determine if locally set environmental thresholds are being met. Indicators on landscape composition, configuration and naturalness explained more than 30% of the variation in ESS supply. Landscape elements such as largely connected forest patches or more complex agricultural patches, e.g., mosaics with shrub and grassland patches, may enhance ESS supply in both of the bioenergy production systems. If tradeoffs between biomass production and other ESS are not addressed by landscape planning, it may be reasonable to include rules in certification schemes that require, e.g., the connectivity of natural or semi-natural forest patches in plantation forestry or semi-natural landscape elements in agricultural production systems. Integrating indicators on landscape configuration and composition into certification schemes is particularly relevant considering that certification schemes are governance tools used to ensure comparable sustainability standards for biomass produced in countries with variable or absent legal frameworks for landscape planning.</p></div

ESS supply (arithmetic mean) for the entire watershed (a, e), natural or semi-natural forest as counterfactual (b, f) for plantation forestry (c) and corn and wheat production (g).

<p>The highest arithmetic mean value for each ESS category is used maximum to scale the radar charts for the Satilla (a-d) and Big Sunflower watersheds (e-h) separately.</p

Variation partitioning for ESS supply in the Satilla (a, c) and Big Sunflower (b, d) watersheds without geographic location (a-b) and with geographic location (c-d).

<p>p<0.01 and values <0 are not shown; the indicated values display the variance captured by the indicator groups on landscape composition, naturalness, landscape configuration, topography and soil parameters selected from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.t001" target="_blank">Table 1</a>; the indicators were selected based on permutation p-values; the variance is captured as adjusted R² for single (non-overlapping areas) and combined categories (overlapping areas).</p

Factors characterizing sufficient and insufficient ESS supply in the Big Sunflower watershed (backward logistic regression).

<p>A positive value for the standardized β indicates that an explanatory variable is contributing to sufficient ESS supply; a negative value for the standardized β indicates that an explanatory variable is contributing to insufficient ESS supply.</p><p>Factors characterizing sufficient and insufficient ESS supply in the Big Sunflower watershed (backward logistic regression).</p

Mapped ESS supply in the Satilla (a-e) and Big Sunflower (f-j) watersheds, also shown as boxplots (k-o).

<p>Mapped P and Sediment retention are plotted with breaks at 0.1, 1, 10 and 100 for better visualization.</p

Validation of modeled annual P (a) and sediment export (b) with measured water quality parameters, total P and total suspended solids, and annual groundwater recharge rates with existing studies (c-d).

<p>A Turkey boxplot is used for the groundwater recharge rates from an existing model [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref090" target="_blank">90</a>] and a range is indicated for existing studies [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref091" target="_blank">91</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref092" target="_blank">92</a>]. The station and watershed names are listed in brackets.</p

Potential variables explaining ESS supply.

<p>Potential variables explaining ESS supply.</p

Sustainability thresholds for P and sediment export set by environmental protection agencies in Georgia and Mississippi with public consultation.

<p>Sustainability thresholds for P and sediment export set by environmental protection agencies in Georgia and Mississippi with public consultation.</p

Potential sustainable biomass availability (calculations based on [38,39,49,50,136,137]) and emission savings in t CO₂ equivalent (emissions factors (CO₂ and CH₄, [51]) for the Mississippi Delta in 2006.

<p>Wheat and corn residues in the Mississippi Delta may contribute up to 0.4% of the potentially available residues of 27 million t dry matter in the entire US in 2012 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref039" target="_blank">39</a>].</p><p>Potential sustainable biomass availability (calculations based on [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref038" target="_blank">38</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref039" target="_blank">39</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref049" target="_blank">49</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref050" target="_blank">50</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref136" target="_blank">136</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref137" target="_blank">137</a>]) and emission savings in t CO₂ equivalent (emissions factors (CO₂ and CH₄, [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116336#pone.0116336.ref051" target="_blank">51</a>]) for the Mississippi Delta in 2006.</p