Predicting height growth in bean plants using non-linear and polynomial models
Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. Th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais
2022-02-01
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| Series: | Revista Agrogeoambiental |
| Subjects: | |
| Online Access: | https://agrogeoambiental.ifsuldeminas.edu.br/index.php/Agrogeoambiental/article/view/1625 |
| _version_ | 1818251421516038144 |
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| author | Ariana Campos Frühauf Edilson Marcelino Silva Tales Jesus Fernandes Joel Augusto Muniz |
| author_facet | Ariana Campos Frühauf Edilson Marcelino Silva Tales Jesus Fernandes Joel Augusto Muniz |
| author_sort | Ariana Campos Frühauf |
| collection | DOAJ |
| description |
Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data.
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| first_indexed | 2024-12-12T16:08:01Z |
| format | Article |
| id | doaj.art-c91870dae1c54e469f2a929d1d664ae2 |
| institution | Directory Open Access Journal |
| issn | 1984-428X 2316-1817 |
| language | English |
| last_indexed | 2024-12-12T16:08:01Z |
| publishDate | 2022-02-01 |
| publisher | Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais |
| record_format | Article |
| series | Revista Agrogeoambiental |
| spelling | doaj.art-c91870dae1c54e469f2a929d1d664ae22022-12-22T00:19:15ZengInstituto Federal de Educação, Ciência e Tecnologia do Sul de Minas GeraisRevista Agrogeoambiental1984-428X2316-18172022-02-0113310.18406/2316-1817v13n320211625Predicting height growth in bean plants using non-linear and polynomial modelsAriana Campos Frühauf0Edilson Marcelino Silva1Tales Jesus Fernandes2Joel Augusto Muniz3Universidade Federal de LavrasUniversidade Federal de LavrasUniversidade Federal de LavrasUniversidade Federal de Lavras Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data. https://agrogeoambiental.ifsuldeminas.edu.br/index.php/Agrogeoambiental/article/view/1625Growth curve. Logistics. Regression. |
| spellingShingle | Ariana Campos Frühauf Edilson Marcelino Silva Tales Jesus Fernandes Joel Augusto Muniz Predicting height growth in bean plants using non-linear and polynomial models Revista Agrogeoambiental Growth curve. Logistics. Regression. |
| title | Predicting height growth in bean plants using non-linear and polynomial models |
| title_full | Predicting height growth in bean plants using non-linear and polynomial models |
| title_fullStr | Predicting height growth in bean plants using non-linear and polynomial models |
| title_full_unstemmed | Predicting height growth in bean plants using non-linear and polynomial models |
| title_short | Predicting height growth in bean plants using non-linear and polynomial models |
| title_sort | predicting height growth in bean plants using non linear and polynomial models |
| topic | Growth curve. Logistics. Regression. |
| url | https://agrogeoambiental.ifsuldeminas.edu.br/index.php/Agrogeoambiental/article/view/1625 |
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