Technical Efficiency of Dairy Farms in Sierra Andina Using Neural Network Modeling

Carlos S. Torres-Inga; Gonzalo López-Crespo; Raúl Guevara-Viera; Jhonny Narváez-Terán; Víctor Guillermo Serpa-García; Clelia Kathrine Guzmán-Espinoza; Guillermo Guevara-Viera; Ángel J Aguirre de Juana

Carlos S. Torres-Inga Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Gonzalo López-Crespo Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Raúl Guevara-Viera Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Jhonny Narváez-Terán Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Víctor Guillermo Serpa-García Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Clelia Kathrine Guzmán-Espinoza Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Guillermo Guevara-Viera Faculty of Agricultural Sciences, University of Cuenca, Ecuador
Ángel J Aguirre de Juana Prometheus Project, Department of Higher Education, Science, Technology, and Innovation, Ecuador

Palabras clave: bovinos lecheros, límites de producción, perceptrón multicapa, modelado

Resumen

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.

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AGSO (2016). Ecuador produce 5.5 millones de leche. Asociación de Ganaderos de la Sierra y el Oriente. Retrieved on March 23, 2017, from http://agsosite.com/2015/10/ecuador-produce-5-5-millones-de-leche.

AREAL, F. J.; TIFFIN, R. y BALCOMBE, K. (2012). Farm Technical Efficiency Under a Tradable Milk Quota System. Journal of dairy science, 95 (1), 50-62.

ATHANASSOPOULOS, A. D. y CURRAM, S. P. (1996). A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units. Journal of the Operational Research Socie-ty, 47 (8), 1000-1016.

BRAVO-URETA, B. E.; SOLÍS, D.; LÓPEZ, V. H. M.; MARIPANI, J. F.; THIAM, A. y RIVAS, T. (2007). Technical Efficiency in Farming: a Meta-Regression Analysis. Journal of productivity Anal-ysis, 27 (1), 57-72.

CALLOW, M. N.; GOBIUS, N. y HETHERINGTON, G. (2005). Development of Profitable Milk Production Systems for Northern Australia: an Analysis of In-tensification of Current Systems. Australian Farm Business Management Journal, 2 (1), 24-37.

CARREÑO, L.; FRANK, F. C. y VIGLIZZO, E. F. (2012). Tradeoffs Between Economic and Ecosystem Ser-vices in Argentina During 50 Years of Land-Use Change. Agriculture, Ecosystems & Environment, 154 (1), 68-77.

CHANG, H. H. y MISHRA, A. K. (2011). Does the Milk Income Loss Contract Program Improve the Tech-nical Efficiency of US Dairy Farms? Journal of dairy science, 94 (6), 2945-2951.

CHARNES, A.; COOPER, W. W. y RHODES, E. (1978). Measuring the Efficiency of Decision Making Units. European journal of operational research, 2 (6), 429-444.

COMERÓN, E. (2012). Eficiencia de los sistemas lech-eros a pastoreo y algunos factores que pueden afectarla. Rosario, Argentina: INTA Rafaela.

COBO, R. y BORROTO, O. (2013). Determinación de la eficiencia bioeconómica de la producción de leche mediante modelos de análisis envolvente de datos. Revista Cubana de Ciencia Agrícola, 47 (3), 233-236

D’HAESE, M.; SPEELMAN, S.; ALARY, V.; TILLARD, E. y D’HAESE, L. (2009). Efficiency in Milk Produc-tion on Reunion Island: Dealing with Land Scarci-ty. Journal of dairy science, 92 (8), 3676-3683.

ESPAC (2016). Encuesta de Superficie y Producción Agropecuaria Continua, Instituto Nacional de Es-tadísticas y Censos, INEC. Retrieved on March 23, 2017, from http://www.ecuadorencifras.gob.ec/estadisticas-agropecuarias-2.

FERNÁNDEZ-NAVARRO, F.; HERVÁS-MARTÍNEZ, C.; GARCÍA-ALONSO, C. y TORRES-JIMÉNEZ, M. (2011). Determination of Relative Agrarian Tech-nical Efficiency by a Dynamic Over-Sampling Pro-cedure Guided by Minimum Sensitivity. Expert Systems with Applications, 38 (10), 12483-12490.

FLORES GUTIÉRREZ, J. O.; HERRERA-TOSCANO, J. y FLORES MÁRQUEZ, S. L. (2014). Cambios en la productividad y sus determinantes en explota-ciones lecheras de Cuba. ITEA-Información Téc-nica Económica Agraria, 110 (2), 187-207.

GALLO, C.; CONTOA, F.; PIERMICHELE L. S.; ANTONAZZOA A. P. (2013). A Neural Network Model for Classifying Olive Farms. Procedia Tech-nology, 8 (2), 593-599.

GUEVARA, G. V.; GUEVARA, R. V.; PEDRAZA, R. O.; MORALES, A. L.; FERNÁNDEZ, N. P. y MORELL, A. C. (2004). Clasificación dinámica de los sistemas de producción lechera de la cuenca Camagüey-Jimaguayú. Rev. Prod. Anim., 16 (1), 25-33.

INCHAISRI, C.; JORRITSMA, R.; VOS, A. M.; VAN DER WEIJDEN, G. C. y HOGEVEEN, H. (2010). Econom-ic Consequences of Reproductive Performance in Dairy Cattle. Theriogenology, 74 (5), 835-846.

JIANG, N. y SHARP, B. (2014). Cost Efficiency of Dairy Farming in New Zealand: a Stochastic Frontier Analysis. Agricultural and Resource Economics Review, 43 (3), 406-418.

GÓMEZ, J. M. (2016). Análisis de la variación de la efi-ciencia en la producción de biocombustibles en América Latina. Estudios gerenciales, 32 (1), 120-126.

MUROVA, O. y CHIDMI. B. (2013). Technical Efficien-cy of US Dairy Farms and Federal Government Programs. Applied Economics, 45 (7), 839-847.

PARLAKAY, O.; SEMERCI, A. y ÇELIK, A. D. (2015). Es-timating Technical Efficiency of Dairy Farms in Turkey: a Case Study of Hatay Province. Custos e Agronegócio Online, 11 (1), 106-115.

SANTIN, D.; DELGADO, F. J. y VALINO, A. (2004). The Measurement of Technical Efficiency: a Neural Network Approach. Applied Economics, 36 (6), 627-635.

THEODORIDIS, A.; RAGKOS, A.; ROUSTEMIS, D.; GALANOPOULOS, K.; ABAS, Z. y SINAPIS, E. (2012). Assessing Technical Efficiency of Chios Sheep Farms with Data Envelopment Analysis. Small Ruminant Research, 107 (2), 85-91.

TIMMER, C. P. (1971). Using a Probabilistic Frontier Production Function to Measure Technical Effi-ciency. Journal of Political Economy, 79 (4), 776-794.

TORRES-INGA, C. S.; GUEVARA, G. V.; GUEVARA, R. V. y AGUIRRE, A. J. (2016). Eficiencia técnica de la producción lechera en granjas bovinas de los Andes centrales. IV Congreso Internacional de Economía: Equidad, Desarrollo Regional y Política Económica, Universidad de Cuenca, Ecuador.