Technical efficiency of dairy farms in sierra andina using neural network modeling

Abstract

The aim of this paper was to estimate the efficiency of milk production of 1 168 cases in Ecuadoran Sierra Sur Andina, with the implementation of a neural network model with multilayer perceptrons. These cases were collected from secondary samples provided by the Official Institute of National Statistics of Ecuador, in 2016. The variables chosen for the model were total milk production on the previous day (P), as dependent variable, and total cattle heads (CH), total laborers in the field (E), and total area attended by laborer (S), as independent variables. The data from individual cases and their impact on the dependent variable were used as the variable selection criteria. The average efficiency was 8.11%, from which the total efficient cases detected (>0.70) were 11 (0.9% of the sample). Later, the cases studied were classified into three groups, depending on the estimated efficiency: Group 1 (≤ 0.4 efficiency); Group 2 (>0.4-≤0.7 efficiency); and Group 3 (>0.7 efficiency). A comparison produced several statistical differences (P<0.01) for variables total milk production/year on the farm, total field laborers, farm size, total cows, total cattle heads, calvings, pregnant cows, and served cows.