Modelización de la captura y fases de desarrollo de la pesquería de la jaiba azul (Callinectes sapidus) en la Laguna Madre, Tamaulipas, México

Rodríguez-Castro, Jorge Homero; Correa-Sandoval, Alfonso; Ramírez-de-León, José Alberto; Adame-Garza, Jorge Alejandro

doi:10.29059/cienciauat.v12i1.775

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The choice of the best model with and without intervention was parsimoniously suggested by Rodríguez et al (2017), through the Akaike Information Criterion (AIC) and the Schwarz Bayesian Criterion (SBC) that compare the goodness of fit of the different models. Both criteria are based on the use of the sum of squared errors and seek to minimize it, based on various combinations of p and q.…”

Section: Methodsmentioning

confidence: 99%

INTERVENTION AND FORECAST MODELS FOR THE PRICE PAID TO PRODUCER OF BEE (Apis mellifera L.) HONEY IN MEXICO

Luis-Rojas¹,

Sánchez²,

García-Mata³

et al. 2022

agro

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Bee (Apis mellifera L) honey is one of the oldest foods that humans have used. Since ancient times, it has been used as a healthy product due to its sweetening and healing properties. In 2020, Mexico produced 54 121 tons (Mg), which ranked the country as the tenth largest producer in the world. The hypothesis was that current honey prices can be explained by previous prices and that they influence the increase in the population of hives and the production of honey in Mexico. To test this hypothesis, the objective of this research was to develop a forecast model for the annual average prices of honey in Mexico (AAPH). The data comprised the 1966 to 2019 prices and the Box-Jenkins methodology of Autoregressive Integrated Moving Average (ARIMA), with and without intervention, was used. The parameters of the models were estimated with the maximum likelihood method of the SAS® software, while the structural change was calculated with the corresponding library (strucchange) of the R software. A model based on the AAPH series was adapted for the 1966−2019 period and validated with data from 2018 and 2019. The series presents five periods of trend structural changes of AAPH: 1966−1985; 1986−1995; 1996−2003; 2004−2008; and 2009−2019. The best estimated model without intervention was ARIMA (1, 1, 1) and the best model with intervention was ARIMA (1, 1, 0), which indicates that the prices of previous years can explain the AAPH. The predictions had a mean absolute percentage error (MAPE) of 8.16 % for the model without intervention and 4.02 % for the model with intervention. Both estimated models suggested that the AAPH have an upward trend in the medium term. The ARIMA model with intervention provided a more accurate estimation of the AAPH and information to plan and make decisions for the next five years.

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Section: Methodsmentioning

confidence: 99%

INTERVENTION AND FORECAST MODELS FOR THE PRICE PAID TO PRODUCER OF BEE (Apis mellifera L.) HONEY IN MEXICO

Luis-Rojas¹,

Sánchez²,

García-Mata³

et al. 2022

agro

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Propiedades de gelificación de carne de jaiba (Callinectes sapidus) adicionada con gelatina y transglutaminasa microbiana

Cortés Morales,

Martínez Maldonado,

Morales Sánchez

et al. 2024

BIOTECNIA

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Las proteínas de la carne de cangrejo generalmente se separan de su caparazón después de la cocción, lo que implica una agregación térmica tipo coágulo, que induce problemas relacionados con su capacidad de retención de agua, lo que limita sus alternativas de procesamiento posteriores. El objetivo de este estudio fue determinar el efecto de la adición de gelatina y transglutaminasa microbiana (MTgasa) sobre las propiedades de geles obtenidos de carne de jaiba azul (Callinectes sapidus) previamente cocida. La carne de jaiba que se sometió a 3 ciclos de lavado-prensado con agua fría, utilizando una relación 3:1 agua-carne y se mezcló con 0 a 3 % de gelatina y 0.5 % de MTgasa, se embutió en tubos de acero inoxidable y se cocieron a 90 ºC durante 15 min en agua caliente. Se determinaron las propiedades mecánicas (análisis del perfil de textura, módulo de Young y tensión máxima), color, agua extraíble e imágenes micrográficas (SEM). Los resultados mostraron que la gelatina interaccionó con las proteínas de la carne de jaiba y la MTgasa utilizó ambas proteínas como sustrato para formar una red estructurada. Las propiedades mecánicas del gel dependieron del nivel de gelatina añadido, con mejores resultados a menor cantidad. La cantidad de agua extraible se redujo mediante la adición de 3 % de gelatina.

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Modelización de la captura y fases de desarrollo de la pesquería de la jaiba azul (Callinectes sapidus) en la Laguna Madre, Tamaulipas, México

Cited by 2 publications

References 19 publications

INTERVENTION AND FORECAST MODELS FOR THE PRICE PAID TO PRODUCER OF BEE (Apis mellifera L.) HONEY IN MEXICO

INTERVENTION AND FORECAST MODELS FOR THE PRICE PAID TO PRODUCER OF BEE (Apis mellifera L.) HONEY IN MEXICO

Propiedades de gelificación de carne de jaiba (Callinectes sapidus) adicionada con gelatina y transglutaminasa microbiana

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