Mathematical modeling of climatological data to estimate passion fruit crop yield (Passiflora edulis L. f. Flavicarpa y purpurea)
Abstract Passion fruit crop yield depends on the behavior of climatic variables, and modeling the dependence relationship of these variables regarding crop yield offers information aimed at facilitating agribusiness decision making. As main aim, passion fruit crop yield was estimated using mathematical models. A multivariate and univariate statistical analysis of meteorological variables was carried out during the observation period between 2007 and 2014 of selected weather stations, identified and located in the Colombian middle tropics (County of Huila). The relationship between yield with the following agroclimatic variables were analyzed: temperature, sunlight, relative humidity, rainfall and ENSO at monthly resolution with empirical and mechanistic models, recommended in scientific literature. Results showed that the multiple regression model requires the highest yield peaks; the adjustment of the multiple regression model is low, while univariate models such as the ARIMA model showed better adjustment in the time series analyzed. The Stewart’s water-yield model has better performance to estimate yield as a function of evapotranspiration in the different phenological phases.
| Auteurs principaux: | , , |
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| Format: | Digital revista |
| Langue: | English |
| Publié: |
Sociedade Brasileira de Fruticultura
2021
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| Accès en ligne: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-29452021000306001 |
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| Résumé: | Abstract Passion fruit crop yield depends on the behavior of climatic variables, and modeling the dependence relationship of these variables regarding crop yield offers information aimed at facilitating agribusiness decision making. As main aim, passion fruit crop yield was estimated using mathematical models. A multivariate and univariate statistical analysis of meteorological variables was carried out during the observation period between 2007 and 2014 of selected weather stations, identified and located in the Colombian middle tropics (County of Huila). The relationship between yield with the following agroclimatic variables were analyzed: temperature, sunlight, relative humidity, rainfall and ENSO at monthly resolution with empirical and mechanistic models, recommended in scientific literature. Results showed that the multiple regression model requires the highest yield peaks; the adjustment of the multiple regression model is low, while univariate models such as the ARIMA model showed better adjustment in the time series analyzed. The Stewart’s water-yield model has better performance to estimate yield as a function of evapotranspiration in the different phenological phases. |
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