Incorporation of environmental covariates to nonlinear mixed models describing fruit growth
Yield prediction is still a major challenge in pear production. Forecasting fruit growth after modeled curves allows predicting both potential yield and quality. This research aimed to fit multilevel no-linear mixed models (NLMM) based on logistic curves to describe pear growth in the Upper valley o...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Ediciones INTA
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/16648 https://doi.org/10.58149/14h1-sp68 |
| _version_ | 1855485815116791808 |
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| author | Del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Reeb, Pablo Daniel |
| author_browse | Bramardi, Sergio Jorge Del Brio, Dolores Reeb, Pablo Daniel Tassile, Valentin |
| author_facet | Del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Reeb, Pablo Daniel |
| author_sort | Del Brio, Dolores |
| collection | INTA Digital |
| description | Yield prediction is still a major challenge in pear production. Forecasting fruit growth after modeled curves allows predicting both potential yield and quality. This research aimed to fit multilevel no-linear mixed models (NLMM) based on logistic curves to describe pear growth in the Upper valley of Rio Negro and Neuquén, Argentina. The models incorporated several thermo-accumulative indices accounting for temperature effects on fruit-growth physiology. In this way, they captured normal fruit-growth patterns and environmental variations along the growing season. The study was conducted on “William´s” pear trees for 16 seasons. Many trees and fruits were randomly selected and identified. Equatorial diameters were weekly measured with an electronic digital caliper. Climatic data was recorded for all studied seasons from INTA Upper Valley agrochemical station and thermo accumulative indexes were calculated from daily temperature. The best models were selected according to the information criteria index. Multilevel NLMM discerned and quantified the sources of stochastic variability at different levels, allowing better index criteria in comparison to models only considering a single level of variability among random effects. The incorporation of thermo accumulative indexes also increased model performance. |
| format | Artículo |
| id | INTA16648 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Ediciones INTA |
| publisherStr | Ediciones INTA |
| record_format | dspace |
| spelling | INTA166482024-02-16T12:37:30Z Incorporation of environmental covariates to nonlinear mixed models describing fruit growth Del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Reeb, Pablo Daniel Pera Crecimiento Medio Ambiente Rendimiento Métodos Estadísticos Pears Growth Environment Yields Statistical Methods Yield prediction is still a major challenge in pear production. Forecasting fruit growth after modeled curves allows predicting both potential yield and quality. This research aimed to fit multilevel no-linear mixed models (NLMM) based on logistic curves to describe pear growth in the Upper valley of Rio Negro and Neuquén, Argentina. The models incorporated several thermo-accumulative indices accounting for temperature effects on fruit-growth physiology. In this way, they captured normal fruit-growth patterns and environmental variations along the growing season. The study was conducted on “William´s” pear trees for 16 seasons. Many trees and fruits were randomly selected and identified. Equatorial diameters were weekly measured with an electronic digital caliper. Climatic data was recorded for all studied seasons from INTA Upper Valley agrochemical station and thermo accumulative indexes were calculated from daily temperature. The best models were selected according to the information criteria index. Multilevel NLMM discerned and quantified the sources of stochastic variability at different levels, allowing better index criteria in comparison to models only considering a single level of variability among random effects. The incorporation of thermo accumulative indexes also increased model performance. El pronóstico de cosecha es un gran desafío en la producción de peras. Estimar el tamaño de los frutos a partir de curvas de crecimiento permite predecir tanto la cantidad como la calidad de la fruta para cosecha. Este trabajo tuvo como objetivo ajustar modelos mixtos no lineales multiniveles (MMNL) basados en la curva logística para describir el crecimiento de peras “William´s” en el Alto Valle de Río Negro y Neuquén, Argentina. Los modelos ajustados incorporaron diferentes índices termoacumulativos que contemplan los efectos de la temperatura en la fisiología del crecimiento de los frutos. De esta manera, se logra no solo describir el crecimiento de los frutos, sino también se pueden observar las variaciones ambientales a lo largo de las temporadas de crecimiento. El estudio se realizó en perales “William´s” durante 16 temporadas. Se seleccionaron e identificaron al azar numerosos árboles y frutos. A cada fruto se le midió su diámetro ecuatorial semanalmente con un calibre digital electrónico. Los datos climáticos se obtuvieron de la estación meteorológica del INTA Alto Valle y los índices termoacumulativos se calcularon a partir de los datos de temperaturas. Los mejores modelos fueron seleccionados según los criterios de información. El MMNL multinivel permitió discernir y cuantificar las fuentes de variabilidad estocástica en diferentes niveles, lo que permitió obtener mejores criterios de información en comparación con los modelos que solo consideraron un único nivel de variabilidad entre los efectos aleatorios. La incorporación de índices termoacumulativos mejoró notablemente la performance de los modelos obtenidos. EEA Alto Valle Fil: Del Brío, Dolores. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Alto Valle; Argentina Fil: Tassile, Valentín. Universidad Nacional del Comahue. Facultad de Ciencias y Tecnología de los Alimentos; Argentina Fil: Bramardi, Sergio Jorge. Universidad Nacional del Comahue. Departamento de Estadística; Argentina Fil: Reeb, Pablo Daniel. Universidad Nacional del Comahue. Departamento de Estadística; Argentina 2024-02-16T12:35:21Z 2024-02-16T12:35:21Z 2023-12 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/16648 0325-8718 1669-2314 https://doi.org/10.58149/14h1-sp68 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Ediciones INTA RIA 49 (3) : 85-92. (diciembre 2023) |
| spellingShingle | Pera Crecimiento Medio Ambiente Rendimiento Métodos Estadísticos Pears Growth Environment Yields Statistical Methods Del Brio, Dolores Tassile, Valentin Bramardi, Sergio Jorge Reeb, Pablo Daniel Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title | Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title_full | Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title_fullStr | Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title_full_unstemmed | Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title_short | Incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| title_sort | incorporation of environmental covariates to nonlinear mixed models describing fruit growth |
| topic | Pera Crecimiento Medio Ambiente Rendimiento Métodos Estadísticos Pears Growth Environment Yields Statistical Methods |
| url | http://hdl.handle.net/20.500.12123/16648 https://doi.org/10.58149/14h1-sp68 |
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