Agriculture Reference
In-Depth Information
production data are not available. Our analysis, therefore, combined
experimental station data with production data from other countries to
predict the size and location of hemp production in Germany. Farm
optimization models (LPs) constructed with a geographical information
system (GIS) allowed the creation of sub-county agricultural land
capabilities reflecting the area's productivity. Farm models constructed for
each of these regions were used to evaluate hemp production's
competitiveness, locally. The analysis determined both where hemp
production would occur and the minimum price at which hemp production
would be competitive on both set-aside and regular farmland. The analysis
also evaluated federal fiscal impacts of various production scenarios.
The following section of the chapter describes data and the
structure of the models used in analysis. Results related to competitiveness
of hemp production in Germany are then presented. The final section
discusses the competitiveness of hemp as an energy source.
2. OBJECTIVE, ANALYTICAL METHODS AND DATA
One objective of the study was to estimate potential hemp production
locations and quantities. A Geographic Information System (GIS) model
was used to construct land units for agricultural land throughout Germany.
ARC-Info software was used in the analysis. ARC-Info allows data to be
organized so that specific areas can be designated on the basis of either
spatial attributes of the data or mathematical combinations of empirical
parameters. ARC-Info processes all geometry forms (
e.g.
dots, lines, areas,
and grids) that can be specified with relevant data parameters. Geometric
data can be subdivided into grid and vector data which allow polygon
overlay so there are no restrictions concerning the size of blended areas.
This tool has been used to analyze specific agriculture issues since the mid
80s (Johnson 1993; Lex 1995; Liebhold and Elkinton 1988; Bill and
Fritsch 1994).
GIS enabled the creation and classification of German agricultural
land into units on the basis of agronomic, climatic, and economic
attributes. A land unit was assigned to agricultural land on the basis of
overlapping or common agronomic and climatic attributes. These units
became the scale of observation for the hemp production analysis. Data
profiles for each land parcel or area were used in linear programming
models to estimate the spatial distribution and production levels of hemp in
Germany under varying price assumptions.
Linear Programming Models (LPMs) were used to estimate hemp
production levels for specific geographic areas, given the productivity of
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