Environmental Engineering Reference
In-Depth Information
-
Shift the commodity/non-commodity relationship off one trajectory into a
mutual decline because of the breaching of an ecological threshold (relevant to
Fig.
2.1c and e
)
Make no alteration to the commodity/non-commodity trajectory but alter the
-
position on that trajectory e.g. concomitant increases in commodity and non-
commodity output in Fig.
2.1a
Alter the strength of the association between commodity and non-commodity
-
outputs e.g. from weak positive jointness to strong positive jointness
An
ex-ante
analysis such as this obviously depends critically on a case-by-case
ex-post empirical investigation of an array of different commodity and non-commodity
output relationships i.e. it's important to understand past relationships between
commodity and non-commodity outputs in order to predict future relationships in
response to policy changes. Furthermore, the origin of jointness as discussed above
is particularly important to aiding an ex-ante analysis. For example, if particular
commodity and non-commodity outputs compete for an allocable fixed input, an
increase in one output could only be achieved by a decline of the other. In the short
run, farms will adapt to policy changes by altering their position on a trajectory.
In the long run, they can alter that linkage, for example by achieving a more rational
use of labour through the adoption of labour saving technologies but this would
require time and investment.
Testing Against Data in Europe
Identification of Jointness
Jointness at the Farm Gate
Some drivers of farm income are drawn from the FADN database and the variables
selected include outputs supplied, inputs used, as well as compensatory payments
for participation in agri-environment programmes and other farm-support programmes.
These data concern only values for commodity outputs and their drivers, because
no data is available from FADN for the provision of non-commodity outputs.
Nevertheless, these data enable a rough comparison of the evolution of the production
structure of the farms and provide insights into how they combine their different
activities.
We analyse the drivers for farm income on a per hectare basis to enable consistent
comparison between farms with quite different sizes. Table
2.3
depicts the regression
coefficients for Eq. (
2.2
):
farm income = b
0
+ b
1
O + b
2
I + b
3
IC + b
4
S + b
5
ES + b
6
ES.O + b
7
ES.I
+ b
8
ES.IC + b
9
ES.S + e
(2.2)
