**2. Reduction of bioenergy industry risk through supply system design**

In bioenergy, the risks are as diverse as the economic agents that make up the industry. From the beginning of the supply chain, the risks farmers face are different from the risks aggregators face. Aggregators, the people who harvest, collect, and transport feedstocks to the biorefinery, face different risks than owners and managers of the biorefinery. While some risks in bioenergy apply across these agents, e.g., the risk that a market for the finished product might not exist, the fact that risk is perspective dependent means that one must be precise about whose risks are under discussion. This section considers one type of risk, supply risk, which biorefinery owners and managers face because of the role that drought and weather variability play.

Risk is a concept to measure 'unwanted' events. At the biorefinery, supply risk means that management must engage in unwanted, costly activity if the chance of insufficient feedstock supply delivered to the biorefinery for conversion materializes; thus the plant cannot run at full capacity. Risk is the probability of an unwanted event occurring multiplied by the consequence [18]. For management, this means that if the feedstock supply is lower than the full capacity of the plant then at least two undesirable events are realized [19, 20]. First, the amount of product created at the plant is reduced meaning that the unit cost of production, and the price necessary to cover costs, increases. The plant must utilize the same amount of

**47**

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition*

resources to run the facility at full capacity as when it runs at less than full capacity, thus driving up unit cost. The second is that, in order to overcome the first undesirable outcome, management must seek out additional sources of feedstock supply. Because the additional supply is not placed under the contract managers have with

Depending on the nature of the risk under analysis, different approaches for mitigation apply [21]. Supply risk is considered non-systematic or diversifiable risk because yield uncertainty is not correlated with risks in other parts of the economic system. For example, crop yield does not correlate with stock market performance but instead with climate variability. For a biorefinery manager, non-systematic risk means that diversification is a strategy to mitigate weather variability. To illustrate how diversifying the feedstock supply allows the manager to mitigate risk, this section proceeds as follows. First, a description of the biomass feedstock supply chain provides a picture of how risk enters the system through yield uncertainty. Then an example illustrates how alternative supply system design enables mitigating supply

Possible biomass supply system configurations are numerous, but are typically classified in two ways: non-distributed and distributed. The non-distributed supply systems, also termed conventional supply systems, have been the systems of choice for the pioneer, or first-built biorefineries [22]. Non-distributed supply systems tend to be vertically integrated with a specific user. This means the biorefinery manages the supply chain from the time the biomass leaves the field to the time it enters the gates of the biorefinery. The materials are delivered in a minimally processed state and the burden of controlling and mitigating feedstock variability is placed on the users at the biorefinery. Non-distributed systems are typically sited in areas with an abundance of easily accessible and low-cost resources. The location of the pioneer biorefineries, as expected, have all been developed in areas with concentrated supplies of biomass known as supply sheds. While supply chains developed using this design are relatively uncomplicated and inexpensive, the biorefineries are limited to a small draw radius, due to the expenses associated with transporting material in the available formats. The relatively small supply shed may impact the ability for the biorefinery operators to mitigate feedstock quality issues with the resources available and potentially not be able to meet resource demands if

The alternative, distributed supply system, sometimes called an advanced supply system, is a series of processing depots or terminals that are used to concentrate material from a small geographic region, near the point of production, and prepare it for use at a single or multiple facilities. This model is similar to how grain elevators work, the grain from local fields is aggregated and sold into a larger market. And, similar to logistics in grain supply systems, the processing depots may be owned by parties other than biorefinery owners. However, instead of simply holding the material for sale, the depots produce a stable, tradable intermediate product, which can be sold in a variety of markets. For a biorefinery, the largest benefit of the distributed supply system is having access to a larger supply shed for material. Biomass quality (e.g., ash and moisture content) is highly variable both spatially and temporally [23]. Through sourcing the material from a series of depots, biorefinery operators are able to specify the desired quality attributes of the material, and the burden of delivering material within the specifications is borne by the owners and operators of the depot. Although the cost of distributed supply systems seems high compared to a non-distributed system, given the

growers for the initial supply, making up the short fall, too, is costly [20].

*DOI: http://dx.doi.org/10.5772/intechopen.85295*

risk caused by weather events, such as drought.

there is a catastrophic event within the supply shed [23, 24].

**2.1 Supply chain**

#### *Drought Impacts on Bioenergy Supply System Risk and Biomass Composition DOI: http://dx.doi.org/10.5772/intechopen.85295*

resources to run the facility at full capacity as when it runs at less than full capacity, thus driving up unit cost. The second is that, in order to overcome the first undesirable outcome, management must seek out additional sources of feedstock supply. Because the additional supply is not placed under the contract managers have with growers for the initial supply, making up the short fall, too, is costly [20].

Depending on the nature of the risk under analysis, different approaches for mitigation apply [21]. Supply risk is considered non-systematic or diversifiable risk because yield uncertainty is not correlated with risks in other parts of the economic system. For example, crop yield does not correlate with stock market performance but instead with climate variability. For a biorefinery manager, non-systematic risk means that diversification is a strategy to mitigate weather variability. To illustrate how diversifying the feedstock supply allows the manager to mitigate risk, this section proceeds as follows. First, a description of the biomass feedstock supply chain provides a picture of how risk enters the system through yield uncertainty. Then an example illustrates how alternative supply system design enables mitigating supply risk caused by weather events, such as drought.

#### **2.1 Supply chain**

*Drought - Detection and Solutions*

drought stress [15–17].

conversion to biofuels and products.

in recent decades [3, 4]. To make matters worse, extreme weather events, like drought, are predicted to become more prevalent under future climate scenarios with corresponding decreases in gross primary productivity [5–8]. The economic impacts of drought are exemplified by the \$30 billion in losses from a recent U.S. nationwide drought in 2012 that primarily impacted the agricultural industry as a result of outcomes such as a 27% reduction in U.S. corn grain yields [9]. These yield losses pose considerable risk for biomass producers and biorefineries that already

Drought conditions lead to increased use of water resources in irrigated areas, but in non-irrigated fields obtaining necessary crop yields is a challenge. Corn, wheat, and barley grain yields have been shown to decrease as a result of drought [11–13]. Of importance to bioenergy technology developers planning to use lignocellulosic biomass, dry biomass yields of corn stover, switchgrass, and *Miscanthus* grown in research plots were reduced in the 2012 drought when compared to yields in 2011 and 2013 [14]. Even crops that have been reported to have some level of drought tolerance, like sorghum and switchgrass, had significant yield reductions during drought, 40–80% in some cases, even though the plants often survive the

Drought is a major risk for producers and biorefineries relying on consistent and high crop yields; however, for the renewable energy industry the effect of drought on crops can be even more substantial and complex. The objective of this chapter is to discuss how biomass destined for renewable energy is affected by drought as it relates to overall dry biomass yields and chemistry, the latter of which heavily impacts cost of production and final product quality. The chapter proceeds with a discussion of how drought related risks impact the supply chain and strategies for risk reduction through thoughtful design of logistics systems for biorefineries. Finally, the chemical analysis of a variety of bioenergy crops grown during severe drought conditions as part of a set of long-term nationwide field trials will be discussed along with the state of knowledge regarding how these changes impact

**2. Reduction of bioenergy industry risk through supply system design**

In bioenergy, the risks are as diverse as the economic agents that make up the industry. From the beginning of the supply chain, the risks farmers face are different from the risks aggregators face. Aggregators, the people who harvest, collect, and transport feedstocks to the biorefinery, face different risks than owners and managers of the biorefinery. While some risks in bioenergy apply across these agents, e.g., the risk that a market for the finished product might not exist, the fact that risk is perspective dependent means that one must be precise about whose risks are under discussion. This section considers one type of risk, supply risk, which biorefinery owners and managers face because of the role that drought and weather

Risk is a concept to measure 'unwanted' events. At the biorefinery, supply risk means that management must engage in unwanted, costly activity if the chance of insufficient feedstock supply delivered to the biorefinery for conversion materializes; thus the plant cannot run at full capacity. Risk is the probability of an unwanted event occurring multiplied by the consequence [18]. For management, this means that if the feedstock supply is lower than the full capacity of the plant then at least two undesirable events are realized [19, 20]. First, the amount of product created at the plant is reduced meaning that the unit cost of production, and the price necessary to cover costs, increases. The plant must utilize the same amount of

have substantial startup challenges to overcome [10].

**46**

variability play.

Possible biomass supply system configurations are numerous, but are typically classified in two ways: non-distributed and distributed. The non-distributed supply systems, also termed conventional supply systems, have been the systems of choice for the pioneer, or first-built biorefineries [22]. Non-distributed supply systems tend to be vertically integrated with a specific user. This means the biorefinery manages the supply chain from the time the biomass leaves the field to the time it enters the gates of the biorefinery. The materials are delivered in a minimally processed state and the burden of controlling and mitigating feedstock variability is placed on the users at the biorefinery. Non-distributed systems are typically sited in areas with an abundance of easily accessible and low-cost resources. The location of the pioneer biorefineries, as expected, have all been developed in areas with concentrated supplies of biomass known as supply sheds. While supply chains developed using this design are relatively uncomplicated and inexpensive, the biorefineries are limited to a small draw radius, due to the expenses associated with transporting material in the available formats. The relatively small supply shed may impact the ability for the biorefinery operators to mitigate feedstock quality issues with the resources available and potentially not be able to meet resource demands if there is a catastrophic event within the supply shed [23, 24].

The alternative, distributed supply system, sometimes called an advanced supply system, is a series of processing depots or terminals that are used to concentrate material from a small geographic region, near the point of production, and prepare it for use at a single or multiple facilities. This model is similar to how grain elevators work, the grain from local fields is aggregated and sold into a larger market. And, similar to logistics in grain supply systems, the processing depots may be owned by parties other than biorefinery owners. However, instead of simply holding the material for sale, the depots produce a stable, tradable intermediate product, which can be sold in a variety of markets. For a biorefinery, the largest benefit of the distributed supply system is having access to a larger supply shed for material. Biomass quality (e.g., ash and moisture content) is highly variable both spatially and temporally [23]. Through sourcing the material from a series of depots, biorefinery operators are able to specify the desired quality attributes of the material, and the burden of delivering material within the specifications is borne by the owners and operators of the depot. Although the cost of distributed supply systems seems high compared to a non-distributed system, given the

requirements for additional infrastructure and increased transportation, systemwide benefits may offset costs [19]. The next section illustrates this point with an example of risk mitigation.

#### **2.2 Mitigating drought risk**

**Figure 1** illustrates both the distributed and non-distributed, stylized supply chain configurations situated on a map of the Midwest United States. The panel on the left shows the location of a biorefinery and 10 potential sites for biomass depots. Multiple processing depots represent the advanced (distributed) case. The black lines illustrate the supply shed radius, which is the geographic area from which biorefinery management collects feedstock. In the conventional (non-distributed) case, the supply radius is 50 miles and the supply shed consists of fields near to the biorefinery. The dotted, black line next to the biorefinery shows the 50 miles radius. Economically constrained by transportation costs, in the conventional case management must contract with growers in near proximity to the biorefinery. On the other hand, the wider, solid black line encompasses the network of depots in the advanced supply case. Because of preprocessing, the economic constraint pushes the supply radius out to 400 miles, thus significantly expanding the supply shed. This enables management to contract with growers at much greater distance. The heat-map shading shows differing levels of drought intensity; red and orange illustrate greater drought intensity and blue a lesser amount.

The Year-A, Year-B designation in the left and right panels, respectively, shows two possible weather outcomes, generated with historical data. In Year-A the map does not show adverse weather events for either supply shed but in Year-B it shows adverse weather in much of the supply shed for both cases. While in Year-A none of the growers in the 50 miles supply shed experience detrimental impacts to crop yield from weather, in Year-B the growers next to the biorefinery collectively face the same adverse weather. By contrast, and looking at the 400 mile supply shed, growers in the northeast of the supply shed do not experience the adverse weather of much of the rest of the supply shed. A simulation model is a useful, analytic tool to understand how weather variability under these two supply chain configurations affect supply risk at the biorefinery.

Suppose management of the biorefinery in **Figure 1** contracts with growers for residual corn stover to procure feedstock to run a biorefinery with nameplate

#### **Figure 1.**

*Comparing two supply chain options under two weather scenarios based on historical data. Year A (left) has no adverse weather events, while year B (right) has moderate to severe drought covering much of the supply shed for both supply chain options [25].*

**49**

up to 1 million tons.

**Figure 2.**

**3.1 Biomass chemical composition**

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition*

*Histogram of simulated feedstock outcomes under two supply chain configurations [25].*

capacity of 800 thousand tons per year. In the conventional case and in the advanced case, management contracts with the same number of growers. In the conventional case farmers face the same distribution of yield uncertainty. In the advanced case the 10 distributions of uncertainty represent 10 separate regions of the supply shed. **Figure 2** shows the histogram of potential outcomes that result from a Monte Carlo simulation of the manager's contract options. The simulation utilizes parameters for yield, ash content, and dry matter loss that are representative of corn stover in the Midwest. The conventional case shows that on average, the manager will receive 751 thousand tons of biomass at the plant, but the range of possibilities extends from as little as 400 thousand tons to just over 1 million tons. In the advanced case, the histogram shows that the manager could expect on average 955 tons of biomass with a range of 800 thousand tons

The results in the histogram illustrate the potential for risk reduction available to the manager by diversifying the supply portfolio. Much like one diversifies a financial retirement portfolio to mitigate risk, advanced supply configurations enable the same strategy. Managers at the biorefinery can mitigate drought-induced supply risk by diversifying the biorefinery's supply portfolio across a larger supply shed.

The biomass supply risks related to drought are substantial and unfortunately extend to biomass quality as well as overall yields as discussed above. Crop yields are often reduced during drought conditions as plants do not have the water needed for basic functions like maintaining cell turgor pressure and performing photosynthesis [26]. The impact of drought conditions on yield as well as plant biochemical functions is complex and different plant types, species, and genotypes may vary in their tolerance and responses to drought [27, 28]. Species like *Miscanthus* are reported to be more sensitive to water deficiencies [29] while crops like sorghum [16], reed canary grass [27], and switchgrass [27, 30] display some level of drought tolerance. In addition, plants use different survival strategies to deal with environmental stressors; for example, there is less carbohydrate hydrolysis in cool-season forbs than in cool-season grasses during osmotic stress that occurs when plants

**3. Drought impacts on chemical composition and conversion**

*DOI: http://dx.doi.org/10.5772/intechopen.85295*

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition DOI: http://dx.doi.org/10.5772/intechopen.85295*

*Drought - Detection and Solutions*

example of risk mitigation.

**2.2 Mitigating drought risk**

requirements for additional infrastructure and increased transportation, systemwide benefits may offset costs [19]. The next section illustrates this point with an

**Figure 1** illustrates both the distributed and non-distributed, stylized supply chain configurations situated on a map of the Midwest United States. The panel on the left shows the location of a biorefinery and 10 potential sites for biomass depots. Multiple processing depots represent the advanced (distributed) case. The black lines illustrate the supply shed radius, which is the geographic area from which biorefinery management collects feedstock. In the conventional (non-distributed) case, the supply radius is 50 miles and the supply shed consists of fields near to the biorefinery. The dotted, black line next to the biorefinery shows the 50 miles radius. Economically constrained by transportation costs, in the conventional case management must contract with growers in near proximity to the biorefinery. On the other hand, the wider, solid black line encompasses the network of depots in the advanced supply case. Because of preprocessing, the economic constraint pushes the supply radius out to 400 miles, thus significantly expanding the supply shed. This enables management to contract with growers at much greater distance. The heat-map shading shows differing levels of drought intensity; red and orange

The Year-A, Year-B designation in the left and right panels, respectively, shows two possible weather outcomes, generated with historical data. In Year-A the map does not show adverse weather events for either supply shed but in Year-B it shows adverse weather in much of the supply shed for both cases. While in Year-A none of the growers in the 50 miles supply shed experience detrimental impacts to crop yield from weather, in Year-B the growers next to the biorefinery collectively face the same adverse weather. By contrast, and looking at the 400 mile supply shed, growers in the northeast of the supply shed do not experience the adverse weather of much of the rest of the supply shed. A simulation model is a useful, analytic tool to understand how weather variability under these two supply chain configurations affect supply risk at the biorefinery. Suppose management of the biorefinery in **Figure 1** contracts with growers for residual corn stover to procure feedstock to run a biorefinery with nameplate

*Comparing two supply chain options under two weather scenarios based on historical data. Year A (left) has no adverse weather events, while year B (right) has moderate to severe drought covering much of the supply* 

illustrate greater drought intensity and blue a lesser amount.

**48**

**Figure 1.**

*shed for both supply chain options [25].*

**Figure 2.** *Histogram of simulated feedstock outcomes under two supply chain configurations [25].*

capacity of 800 thousand tons per year. In the conventional case and in the advanced case, management contracts with the same number of growers. In the conventional case farmers face the same distribution of yield uncertainty. In the advanced case the 10 distributions of uncertainty represent 10 separate regions of the supply shed.

**Figure 2** shows the histogram of potential outcomes that result from a Monte Carlo simulation of the manager's contract options. The simulation utilizes parameters for yield, ash content, and dry matter loss that are representative of corn stover in the Midwest. The conventional case shows that on average, the manager will receive 751 thousand tons of biomass at the plant, but the range of possibilities extends from as little as 400 thousand tons to just over 1 million tons. In the advanced case, the histogram shows that the manager could expect on average 955 tons of biomass with a range of 800 thousand tons up to 1 million tons.

The results in the histogram illustrate the potential for risk reduction available to the manager by diversifying the supply portfolio. Much like one diversifies a financial retirement portfolio to mitigate risk, advanced supply configurations enable the same strategy. Managers at the biorefinery can mitigate drought-induced supply risk by diversifying the biorefinery's supply portfolio across a larger supply shed.
