Agriculture Reference
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commodities by region. Additionally, soils data, climatic data, and county
boundaries were incorporated into the analysis. Regional production and
input price data for the primary commodities produced in each county were
obtained from the Chamber of Agriculture Schleswig-Holstein (1995) and
farm enterprise data from the Institute of Farm Management, University of
Kiel (1995/96).
There are no historical hemp production or input data. Therefore,
we estimated hemp production yields and costs from experimental farm
budget data, hemp test plots data and from Belgium and France production
data (Christen and Schulze 1997; Schulze 1995). Table 9.1 shows selected
input and yield data for hemp under different product use assumptions.
Data on the aggregate size, structure and enterprise mix of
farmland by county were obtained from KTBL-Taschenbuch data
(1994/95); the Chamber of Agriculture, Schleswig-Holstein (1995), and the
Institute of Farm Management, University of Kiel (1995 and 1996). Soil
data were based on a soils map from the German Soil Association
(Brodersen and Drescher 1997). Heat/temperature, precipitation, and frost
data were mapped from data of the German Weather Service (Brodersen
and Drescher 1997). Data to map county boundaries were obtained from
the Institut of Angewandte Geodäsie (Brodersen and Drescher 1997).
3. MODEL CONSTRUCTION
Prediction of the location and level of hemp production at various price
levels required estimation of the net returns from production of both hemp
and commodities that would be profitable in each production area based on
agronomic and economic attributes that influence productivity and cost of
production. Procedures for estimating hemp's competitiveness are
summarized in Figure 9.1. The first step was the construction of land
classes based on agronomic and climatic attributes. GIS analysis was used
to isolate land classes on the basis of soil type, rainfall, frost-free days, and
geographic-group units. Each area, a sub-county unit, had common soil and
climatic attributes. GIS county data were used to estimate yields for the
nine most important crops—winter wheat, winter barley, spring barley,
winter rye, rape seed (canola), oats, sugar beets, corn, and potatoes—
grown in each county. County level land use and crop shares were also
integrated into the model to be used in the production analysis. Crop yield
attributes were then attached to each specific sub-county unit.
Initially, 10,004 different types or land classes were generated.
Cluster analysis, focusing on commodity yields, was used to aggregate the
initial areas into 25 land units reflecting different productivity types, as
well as, production shares for its primary commodities.
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