Modeling Seed Longevity and Percentile Prediction: A Sigmoidal Function Approach in Soybean, Maize, and Tomato

Modeling Seed Longevity and Percentile Prediction: A Sigmoidal Function Approach in Soybean, Maize, and Tomato

This study aims to evaluate the behavior of seed longevity in soybean, maize, and tomato stored under controlled conditions using Logistic and Boltzmann sigmoidal models. Additionally, it seeks to determine the performance of these models in predicting <inline-formula><math xmlns="http...

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Main Authors: Felipe Souza Carvalho, Brunna Rithielly Rezende, Amanda Rithieli Pereira dos Santos, Maria Márcia Pereira Sartori
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:AgriEngineering
Subjects:
logistic model
Boltzmann model
Sigmoid functions
<i>P</i><sub>50</sub>
<i>P</i><sub>85</sub>
<i>P</i><sub>25</sub>
Online Access:https://www.mdpi.com/2624-7402/7/1/5
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Summary:This study aims to evaluate the behavior of seed longevity in soybean, maize, and tomato stored under controlled conditions using Logistic and Boltzmann sigmoidal models. Additionally, it seeks to determine the performance of these models in predicting <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>85</mn></mrow></msub></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>25</mn></mrow></msub></mrow></semantics></math></inline-formula>. The models were fitted to the experimental longevity data, and their performance in predicting the percentiles was evaluated. The Logistic model showed better performance in predicting <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula> (time for viability to drop to 50%), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>85</mn></mrow></msub></mrow></semantics></math></inline-formula> (time for viability to drop to 85%), and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>25</mn></mrow></msub></mrow></semantics></math></inline-formula> (time for viability to drop to 25%), estimating the parameters more frequently within the experimental range (obtained from the initial viability data). The results of this study suggest that some cultivars exhibited different patterns in deterioration rates, with some showing abrupt declines in viability, highlighting differences in the speed and nature of seed deterioration. The Logistic model proved to be superior, with an accuracy of 83% in estimating the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>85</mn></mrow></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>25</mn></mrow></msub></mrow></semantics></math></inline-formula> percentiles, while the Boltzmann model achieved an accuracy of 54%. The tomato cultivar Gaucho showed the greatest loss in germination, reaching <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>25</mn></mrow></msub></mrow></semantics></math></inline-formula> quickly, while the soybean cultivar M 7119 IPRO and maize cultivar MAM06 maintained high germination for a longer period. These findings emphasize the importance of using viability percentiles to optimize storage practices, minimize economic losses, and prevent genetic erosion in conservation programs. Modeling seed longevity using sigmoidal models can significantly contribute to determining various viability percentiles, supporting storage practices and providing valuable insights for strategic decision-making in seed management, proving useful in both commercial and species conservation contexts.
ISSN:2624-7402

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