Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches
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This paper proposes the use of the Generalized Maximum Entropy (GME) method to
estimate input allocation in multi-crop systems using heterogeneous data sources (farm
accountancy data and cropping practices survey data). The aim is to explore the role of
well-defined a priori information in improving the accuracy of GME estimation. The
performance of the GME method is compared afterward to a Bayesian approach—
Highest Posterior Density (HPD)—to assess their accuracy when reliable non-sample
(prior) information is used and investigate their usefulness for reconciling
heterogeneous data sources. Both approaches are applied to a given set of farm
accounting data which reports information on input allocation between alternative
input uses. The estimation results show that the use of well-defined prior information
from external data source improves GME estimates even though this performance is not
always significant. It also appears that the Bayesian (HPD) approach could be a good
alternative to the GME estimator. HPD provides results that are close to the GME
method with the advantage of a straightforward and transparent implementation of the
a priori information.