Agriculture Reference
In-Depth Information
The Choice of Farm Organization. Prediction 9.4 says that as the number of cycles
increases, we predict that family farming will be less common. One important element of
seasonality that can be defined empirically is the number of cycles per year for a given
crop in a given location. We classify the crops in our sample into three categories using
the variable CYCLES: crops that potentially have more than one cycle per year; crops that
always have just one cycle per year; and crops that may have fewer than one cycle per
year. 43 In the equations reported in table 9.1, we include the variables CYCLES
1 and
1, leaving CYCLES = 1 out of the equations. 44 The distinction between beef
and dairy cattle can also be used to test some predictions of our model. Dairy animals are
kept close to their barns so that they can be milked twice a day, while beef animals usually
range in open pastures. Daily milk production is easier to measure than beef production,
and dairy processors engage in exceptional forms of measurement to ensure that the farmer
does not carelessly handle or tamper with the milk. 45 Finally, there are more routine day-to-
day tasks with dairy production than cow-calf beef operations. None of the beef operations
in our sample are large feedlots with similarly routine daily tasks. All of these factors reduce
monitoring costs on dairy farms. In terms of our model, dairy farms have more cycles and
fewer stages than beef operations. Hence, the use of farm managers and partners is more
viable on dairy farms than beef farms. Since the gains from specialization are greater with
more tasks, our model predicts that the probability of family farm organization will be
higher for beef operations (positive coefficient on BEEF) than for dairy operations (negative
coefficient on DAIRY). The omitted category consists of farms with either no stock or
noncattle stock.
Prediction 9.5 says that as the natural stage uncertainty
diminishes, the farm is
less likely to be a family farm. Irrigation can control the effect of nature by reducing
variance in output. In terms of the model, irrigation reduces
2 . The coefficient on the
variable IRRIGATION (percent of farmland irrigated) is predicted to have a negative sign.
The estimated equation also included numerous control variables including the percentage
of rented farmland (RENTED LAND), farmer's age (AGE, AGE2), farmer's education
(EDUCATION, EDUCATION 2 ), and a dummy for British Columbia (BC).
We use the British Columbia-Louisiana data to estimate the determinants of farm organi-
zation choice and test some specific predictions from our model. The empirical specification,
for the complete model is
F i = X i β i + i i =
F i >
F i =
F i
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