Browsing by Author "Aguirre De juana, Angel Javier"

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Analysis of fluid velocity inside an agricultural sprayer using generalized linear mixed models
(2020) Boné, Antonio; Torres Inga, Carlos Santiago; Guevara Viera, Raúl Victorino; Guevara Viera, Guillermo Emilio; Aguirre De juana, Angel Javier; García Ramos, Francisco Javier; Vidal, Mariano
The fluid velocity inside the tank of agricultural sprayers is an indicator of the quality of the mixture. This study aims to formulate the best generalized linear mixed model to infer the fluid velocity inside a tank under specific operational parameters of the agitation system, such as liquid level, circuit pressures, and number of active nozzles. A complex model was developed that included operational parameters as fixed eects (FE) and the section of the tank as the random eect. The goodness of fit of the model was evaluated by considering the lowest values of Akaike's information criteria and Bayesian information criterion, and by estimating the residual variance. The gamma distribution and log-link function enhanced the goodness of fit of the best model. The Toeplitz structure was chosen as the structure of the covariance matrix. SPSS and SAS software were used to compute the model. The analysis showed that the greatest influence on the fluid velocity was exerted by the liquid level in the tank, followed by the circuit pressure and, finally, the number of active nozzles. The development presented here could serve as a guide for formulating models to evaluate the eciency of the agitation system of agricultural sprayers.
Eficiencia técnica en granjas lecheras de la sierra andina mediante modelación con redes neuronales
(2019) Torres Inga, Carlos Santiago; López Crespo, Gonzalo Estuardo; Guevara Viera, Raúl Victorino; Narváez Terán, Jhonny Alfredo; Serpa García, Víctor Guillermo; Guzmán Espinoza, Clelia Kathrine; Guevara Viera, Guillermo Emilio; Aguirre De juana, Angel Javier
Aim: The aim of this work was to estimate the efficiency of milk production in 1 168 cases in Ecuadoran Sierra Sur Andina, with the implementation of neural networks with multilayer perceptrons. Materials and Methods: 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 previ-ous day (P), as dependent variable; and total cattle heads (CH), total laborers in the field (E), and total surface at-tended by laborer (S), as independent variables. The selection criteria were the existence of data from individual cas-es, and their impact on the dependent variable. Results: The average efficiency was 8.11 %, from which the total cases detected efficiently (> 0.70) accounted for 11 (0.9 % of the sample). Later, the cases studied were classified into three groups, depending on the efficiency calculated: Group 1 (≤ 0.4 efficiency); Group 2 (> 0.4 - ≤ 0.7 efficiency); and Group 3 (> 0.7 efficiency). Conclusion: 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.
Technical efficiency of dairy farms in sierra andina using neural network modeling
(2019) Torres Inga, Carlos Santiago; López Crespo, Gonzalo Estuardo; Guevara Viera, Raúl Victorino; Narváez Terán, Jhonny Alfredo; Serpa García, Víctor Guillermo; Guzmán Espinoza, Clelia Kathrine; Guevara Viera, Guillermo Emilio; Aguirre De juana, Angel Javier
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.