Systems Informatics and Analysis - Engineering and Science of Biomass Feedstock Production and Provision - page 204

It is argued that specialized machinery might be needed to harvest novel bioen-
ergy crops. Since farmers are expected to grow energy crops on only a part of the
land, at least initially, farmers will avoid purchasing dedicated machinery and will
instead rely on leased equipment or custom harvesting. In view of this, Bochtis et al.
[ 69 ] proposed an optimization model using the fl ow shop problem formulation,
where the aim was to effi ciently use a limited set of equipment to perform multiple,
sequential tasks on different fi elds in a region. The objective function in the problem
was to minimize the total time requirement. FARMSYS is another farm machinery
management system developed in PROLOG using an object-oriented modeling
approach [ 57 ]. FARMSYS has been evaluated by farmers with satisfactory perfor-
mance but is yet to be applied to study energy crops. Such machinery management
tools will become increasingly important in the future.
Inclement weather impacts farm and machinery operations signifi cantly.
Therefore, Hwang et al. [ 70 ] developed a simple rule-based model to convert
weather data into probabilistic estimates of the mowing and baling days in the state
of Oklahoma, in the USA. They developed a decision-making sequence that classi-
fi ed a day as suitable or not. They also incorporated several smaller models, such as
the soil-water balance model, within this framework. The estimates provided by
such models were to be used by the machinery selection models or whole-farm
simulation models.

8.3.3 Local Production and Provision System

The SIA tools have been commonly used to study the local production and provi-
sion systems, which include on-farm production, transportation, handling, storage,
and fi nal delivery to the biorefi nery gate. In particular, a large number of studies
have conducted case-specifi c, system-level analysis without the development of a
generic model [ 15 , 71 , 72 ]. We focus primarily on studies that involved the devel-
opment of a generic model and possibly supported by informatics and decision
support tools. The review is not exhaustive by any means, and the goal is to
describe some important, novel approaches in this area. Table 8.2 provides a sum-
mary of important results generated using these models. The bioenergy crop,
region of consideration, important scenario features, and important results are
reported in the table.

8.3.3.1 Simulation Models

Simulation-based approaches have been commonly used, and discrete event simula-
tion (DES) has been of particular interest. DES is suitable to model a dynamic and
stochastic system that is dependent upon events happening to entities at discrete
(and possibly random) times in the simulation horizon. From a feedstock produc-
tion perspective, a unit of biomass, such as a bale, or a transportation equipment